IA de Detecção de Vagões Ferroviários

USE CASEANWENDUNGSFALLUSE CASEUSE CASEUSE CASEUSE CASECASO DE USO · Railcar detection · LiDAR perception at the loading stationWaggon-Erkennung · LiDAR-Wahrnehmung an der VerladestationRailcar detection · LiDAR perception at the loading stationRailcar detection · LiDAR perception at the loading stationRailcar detection · LiDAR perception at the loading stationRailcar detection · LiDAR perception at the loading stationDetecção de vagões · percepção LiDAR na estação de carregamento

Wagon position, lid count, sequence number — from a single scan. Waggonposition, Deckelzahl, Sequenznummer — aus einem einzigen Scan. Wagon position, lid count, sequence number — from a single scan. Wagon position, lid count, sequence number — from a single scan. Wagon position, lid count, sequence number — from a single scan. Wagon position, lid count, sequence number — from a single scan. Posição do vagão, contagem de tampas, número de sequência — a partir de uma única varredura.

Railcar detection AI is a perception pipeline that ingests a single 3D-LiDAR scan of a freight wagon at the loading station and returns the wagon position, its identifying features (lid count, lid centerpoints, the leading roboterwagen if present) and a wagon-number assignment — drives automated loading workflows and per-wagon material logs into ERP.Waggon-Erkennung KI ist eine Wahrnehmungs-Pipeline, die einen einzelnen 3D-LiDAR-Scan eines Güterwagens an der Verladestation verarbeitet und die Waggonposition, die identifizierenden Merkmale (Deckelzahl, Deckel-Mittelpunkte, vorausfahrender Roboterwagen falls vorhanden) sowie die Waggonnummer-Zuordnung zurückgibt — treibt die automatische Verladeautomatisierung und schreibt das Material-Log pro Waggon ins ERP.Railcar detection AI is a perception pipeline that ingests a single 3D-LiDAR scan of a freight wagon at the loading station and returns the wagon position, its identifying features (lid count, lid centerpoints, the leading roboterwagen if present) and a wagon-number assignment — drives automated loading workflows and per-wagon material logs into ERP.Railcar detection AI is a perception pipeline that ingests a single 3D-LiDAR scan of a freight wagon at the loading station and returns the wagon position, its identifying features (lid count, lid centerpoints, the leading roboterwagen if present) and a wagon-number assignment — drives automated loading workflows and per-wagon material logs into ERP.Railcar detection AI is a perception pipeline that ingests a single 3D-LiDAR scan of a freight wagon at the loading station and returns the wagon position, its identifying features (lid count, lid centerpoints, the leading roboterwagen if present) and a wagon-number assignment — drives automated loading workflows and per-wagon material logs into ERP.Railcar detection AI is a perception pipeline that ingests a single 3D-LiDAR scan of a freight wagon at the loading station and returns the wagon position, its identifying features (lid count, lid centerpoints, the leading roboterwagen if present) and a wagon-number assignment — drives automated loading workflows and per-wagon material logs into ERP.A IA de detecção de vagões ferroviários é um pipeline de percepção que ingere uma única varredura 3D-LiDAR de um vagão de carga na estação de carregamento e retorna a posição do vagão, suas características de identificação (contagem de tampas, centros das tampas, o vagão-robô de controle líder, se presente) e uma atribuição de número de vagão — aciona os fluxos automáticos de carregamento e o registro de material por vagão no ERP.

The pain shows up at every industrial plant that loads bulk material onto rail — salt mines, scrap recycling, bulk yards, fertiliser depots. Wagons roll under the loading equipment one by one, and the loading control system needs to know precisely where each wagon is, how many compartments it has, and when it is full. Today operators do this visually: peering over the rail, eyeballing lid positions, ticking wagons off on a clipboard. It is slow, error-prone and unforgiving — the wrong lid gets loaded, the wrong wagon gets billed, the ERP entry does not match the actual yard state, and the shift loses an hour reconciling paper against reality.Den Schmerz kennt jeder Industriebetrieb, der Schüttgut auf die Schiene verlädt — Salzbergwerke, Schrott-Recycling, Schüttgut-Höfe, Düngemittel-Depots. Güterwagen rollen einzeln unter die Verladeeinrichtung, und die Verladesteuerung muss exakt wissen, wo jeder Waggon steht, wie viele Kammern er hat und wann er voll ist. Heute machen Bediener das per Augenmaß: über die Schiene gebeugt, Deckelpositionen geschätzt, Waggons auf dem Klemmbrett abgehakt. Es ist langsam, fehleranfällig und gnadenlos — der falsche Deckel wird beladen, der falsche Waggon abgerechnet, das ERP passt nicht zum tatsächlichen Hof-Zustand, und die Schicht verliert eine Stunde damit, Papier gegen Realität abzugleichen.The pain shows up at every industrial plant that loads bulk material onto rail — salt mines, scrap recycling, bulk yards, fertiliser depots. Wagons roll under the loading equipment one by one, and the loading control system needs to know precisely where each wagon is, how many compartments it has, and when it is full. Today operators do this visually: peering over the rail, eyeballing lid positions, ticking wagons off on a clipboard. It is slow, error-prone and unforgiving — the wrong lid gets loaded, the wrong wagon gets billed, the ERP entry does not match the actual yard state, and the shift loses an hour reconciling paper against reality.The pain shows up at every industrial plant that loads bulk material onto rail — salt mines, scrap recycling, bulk yards, fertiliser depots. Wagons roll under the loading equipment one by one, and the loading control system needs to know precisely where each wagon is, how many compartments it has, and when it is full. Today operators do this visually: peering over the rail, eyeballing lid positions, ticking wagons off on a clipboard. It is slow, error-prone and unforgiving — the wrong lid gets loaded, the wrong wagon gets billed, the ERP entry does not match the actual yard state, and the shift loses an hour reconciling paper against reality.The pain shows up at every industrial plant that loads bulk material onto rail — salt mines, scrap recycling, bulk yards, fertiliser depots. Wagons roll under the loading equipment one by one, and the loading control system needs to know precisely where each wagon is, how many compartments it has, and when it is full. Today operators do this visually: peering over the rail, eyeballing lid positions, ticking wagons off on a clipboard. It is slow, error-prone and unforgiving — the wrong lid gets loaded, the wrong wagon gets billed, the ERP entry does not match the actual yard state, and the shift loses an hour reconciling paper against reality.The pain shows up at every industrial plant that loads bulk material onto rail — salt mines, scrap recycling, bulk yards, fertiliser depots. Wagons roll under the loading equipment one by one, and the loading control system needs to know precisely where each wagon is, how many compartments it has, and when it is full. Today operators do this visually: peering over the rail, eyeballing lid positions, ticking wagons off on a clipboard. It is slow, error-prone and unforgiving — the wrong lid gets loaded, the wrong wagon gets billed, the ERP entry does not match the actual yard state, and the shift loses an hour reconciling paper against reality.A dor aparece em todas as plantas industriais que carregam material a granel em ferrovia — minas de sal, reciclagem de sucata, pátios de granéis, depósitos de fertilizantes. Os vagões passam sob o equipamento de carregamento um a um, e o sistema de controle de carga precisa saber exatamente onde cada vagão está, quantos compartimentos ele tem e quando está cheio. Hoje os operadores fazem isso visualmente: olhando por cima do trilho, medindo as tampas a olho, marcando vagões numa prancheta. É lento, propenso a erros e implacável — a tampa errada é carregada, o vagão errado é faturado, o lançamento no ERP não bate com o estado real do pátio, e o turno perde uma hora reconciliando papel com a realidade.

Our approach is one LiDAR mounted above the loading track. As each wagon stops, the sensor captures a single scan; a six-stage stateless geometric pipeline — ground crop → deck-Z estimation → height-band masking → lid clustering → wagon-bounds → tracker state machine — returns the wagon's exact 3D position, all lid centerpoints, the leading Roboterwagen if it is in frame, and a sequence number. The geometry stages are pure NumPy and scipy in the prototype and port cleanly to C++/C# for the final plant controller. Mean centroid error stays under 5 cm against hand-labelled ground truth; the whole scan is processed in under two seconds on commodity hardware.Unser Ansatz ist ein LiDAR, montiert über dem Verladegleis. Sobald ein Waggon hält, nimmt der Sensor einen einzigen Scan auf; eine sechsstufige zustandslose geometrische Pipeline — Ground-Crop → Deck-Z-Schätzung → Höhenband-Maskierung → Deckel-Clustering → Waggon-Bounds → Tracker-Zustandsautomat — liefert die exakte 3D-Position des Waggons, alle Deckel-Mittelpunkte, den vorausfahrenden Roboterwagen falls im Bild und eine Sequenznummer. Die Geometriestufen sind im Prototyp reines NumPy und scipy und portieren sauber nach C++/C# für den finalen Anlagencontroller. Der mittlere Mittelpunkts-Fehler bleibt unter 5 cm gegenüber handgelabelter Ground Truth; der gesamte Scan wird in unter zwei Sekunden auf Standard-Hardware verarbeitet.Our approach is one LiDAR mounted above the loading track. As each wagon stops, the sensor captures a single scan; a six-stage stateless geometric pipeline — ground crop → deck-Z estimation → height-band masking → lid clustering → wagon-bounds → tracker state machine — returns the wagon's exact 3D position, all lid centerpoints, the leading Roboterwagen if it is in frame, and a sequence number. The geometry stages are pure NumPy and scipy in the prototype and port cleanly to C++/C# for the final plant controller. Mean centroid error stays under 5 cm against hand-labelled ground truth; the whole scan is processed in under two seconds on commodity hardware.Our approach is one LiDAR mounted above the loading track. As each wagon stops, the sensor captures a single scan; a six-stage stateless geometric pipeline — ground crop → deck-Z estimation → height-band masking → lid clustering → wagon-bounds → tracker state machine — returns the wagon's exact 3D position, all lid centerpoints, the leading Roboterwagen if it is in frame, and a sequence number. The geometry stages are pure NumPy and scipy in the prototype and port cleanly to C++/C# for the final plant controller. Mean centroid error stays under 5 cm against hand-labelled ground truth; the whole scan is processed in under two seconds on commodity hardware.Our approach is one LiDAR mounted above the loading track. As each wagon stops, the sensor captures a single scan; a six-stage stateless geometric pipeline — ground crop → deck-Z estimation → height-band masking → lid clustering → wagon-bounds → tracker state machine — returns the wagon's exact 3D position, all lid centerpoints, the leading Roboterwagen if it is in frame, and a sequence number. The geometry stages are pure NumPy and scipy in the prototype and port cleanly to C++/C# for the final plant controller. Mean centroid error stays under 5 cm against hand-labelled ground truth; the whole scan is processed in under two seconds on commodity hardware.Our approach is one LiDAR mounted above the loading track. As each wagon stops, the sensor captures a single scan; a six-stage stateless geometric pipeline — ground crop → deck-Z estimation → height-band masking → lid clustering → wagon-bounds → tracker state machine — returns the wagon's exact 3D position, all lid centerpoints, the leading Roboterwagen if it is in frame, and a sequence number. The geometry stages are pure NumPy and scipy in the prototype and port cleanly to C++/C# for the final plant controller. Mean centroid error stays under 5 cm against hand-labelled ground truth; the whole scan is processed in under two seconds on commodity hardware.Nossa abordagem usa um LiDAR montado sobre o trilho de carregamento. Assim que cada vagão para, o sensor captura uma única varredura; um pipeline geométrico sem estado, de seis estágios — corte do solo → estimativa de Z do deck → mascaramento por faixa de altura → clusterização das tampas → contornos do vagão → máquina de estados do rastreador — retorna a posição 3D exata do vagão, todos os centros de tampa, o vagão-robô de controle líder se estiver no quadro e um número de sequência. Os estágios geométricos são NumPy e scipy puros no protótipo e portam-se de forma limpa para C++/C# no controlador final da planta. O erro médio de centroide fica abaixo de 5 cm em relação ao ground truth rotulado à mão; a varredura inteira é processada em menos de dois segundos em hardware comum.

This engagement descends from Industrial Perception AI and is the one use case where we already shipped — the Bahnwagon-Deckelerkennung pipeline runs in production at a north-German recycling and mining operation. The same geometric approach works whether the wagon carries rock, salt, scrap or grain; only the calibration constants in config.yaml change, no model retraining required.Das Projekt geht aus unserem Hub-Service Industrielle Objekterkennung hervor und ist der eine Anwendungsfall, in dem wir bereits ausgeliefert haben — die Bahnwagon-Deckelerkennung läuft produktiv bei einem norddeutschen Recycling- und Bergbaubetrieb. Derselbe geometrische Ansatz funktioniert unabhängig davon, ob der Waggon Stein, Salz, Schrott oder Getreide trägt; nur die Kalibrierungskonstanten in config.yaml ändern sich, ein Modell-Retraining ist nicht nötig.This engagement descends from Industrial Perception AI and is the one use case where we already shipped — the Bahnwagon-Deckelerkennung pipeline runs in production at a north-German recycling and mining operation. The same geometric approach works whether the wagon carries rock, salt, scrap or grain; only the calibration constants in config.yaml change, no model retraining required.This engagement descends from Industrial Perception AI and is the one use case where we already shipped — the Bahnwagon-Deckelerkennung pipeline runs in production at a north-German recycling and mining operation. The same geometric approach works whether the wagon carries rock, salt, scrap or grain; only the calibration constants in config.yaml change, no model retraining required.This engagement descends from Industrial Perception AI and is the one use case where we already shipped — the Bahnwagon-Deckelerkennung pipeline runs in production at a north-German recycling and mining operation. The same geometric approach works whether the wagon carries rock, salt, scrap or grain; only the calibration constants in config.yaml change, no model retraining required.This engagement descends from Industrial Perception AI and is the one use case where we already shipped — the Bahnwagon-Deckelerkennung pipeline runs in production at a north-German recycling and mining operation. The same geometric approach works whether the wagon carries rock, salt, scrap or grain; only the calibration constants in config.yaml change, no model retraining required.Este trabalho descende do serviço Industrial Perception AI e é o único caso de uso em que já entregamos — o pipeline Bahnwagon-Deckelerkennung está em produção em uma operação de reciclagem e mineração no norte da Alemanha. A mesma abordagem geométrica funciona quer o vagão carregue rocha, sal, sucata ou grãos; só mudam as constantes de calibração no config.yaml, sem retreinamento de modelo.

„One scan, six lid centerpoints, wagon number, under two seconds — that is what the loading control wants to see." „Ein Scan, sechs Deckel-Mittelpunkte, Waggonnummer, unter zwei Sekunden — genau das will die Verladesteuerung sehen." „One scan, six lid centerpoints, wagon number, under two seconds — that is what the loading control wants to see." „One scan, six lid centerpoints, wagon number, under two seconds — that is what the loading control wants to see." „One scan, six lid centerpoints, wagon number, under two seconds — that is what the loading control wants to see." „One scan, six lid centerpoints, wagon number, under two seconds — that is what the loading control wants to see." "Uma varredura, seis centros de tampa, número do vagão, menos de dois segundos — é isso que o controle de carga quer ver."

The Bahnwagon-Deckelerkennung pipeline is in production today — six lid centroids, sub-5 cm error, two-second response, Electron + Vue 3 + Three.js operator UI. Die Bahnwagon-Deckelerkennung läuft heute produktiv — sechs Deckel-Mittelpunkte, Fehler unter 5 cm, Antwort in zwei Sekunden, Bediener-UI in Electron + Vue 3 + Three.js. The Bahnwagon-Deckelerkennung pipeline is in production today — six lid centroids, sub-5 cm error, two-second response, Electron + Vue 3 + Three.js operator UI. The Bahnwagon-Deckelerkennung pipeline is in production today — six lid centroids, sub-5 cm error, two-second response, Electron + Vue 3 + Three.js operator UI. The Bahnwagon-Deckelerkennung pipeline is in production today — six lid centroids, sub-5 cm error, two-second response, Electron + Vue 3 + Three.js operator UI. The Bahnwagon-Deckelerkennung pipeline is in production today — six lid centroids, sub-5 cm error, two-second response, Electron + Vue 3 + Three.js operator UI. O pipeline Bahnwagon-Deckelerkennung está em produção hoje — seis centroides de tampa, erro abaixo de 5 cm, resposta em dois segundos, interface de operador em Electron + Vue 3 + Three.js.

Three stagesDrei StufenThree stagesThree stagesThree stagesThree stagesTrês estágios

From wagon stop to ERP entry — three stages of the pipeline. Vom Waggon-Stopp bis zum ERP-Eintrag — drei Stufen der Pipeline. From wagon stop to ERP entry — three stages of the pipeline. From wagon stop to ERP entry — three stages of the pipeline. From wagon stop to ERP entry — three stages of the pipeline. From wagon stop to ERP entry — three stages of the pipeline. Da parada do vagão ao lançamento no ERP — três estágios do pipeline.

1 · LiDAR capture at the loading position1 · LiDAR-Erfassung an der Verladeposition1 · LiDAR capture at the loading position1 · LiDAR capture at the loading position1 · LiDAR capture at the loading position1 · LiDAR capture at the loading position1 · Captura LiDAR na posição de carregamento

One LiDAR mounted above the loading track, IP65+ housing, scans every wagon as it stops at the loading position. No camera required, no second sensor to calibrate. Runs through dust, snow, night light and the steam that comes off warm bulk material. One scan per wagon stop, written to local storage with a per-wagon UUID for audit trail. Ein LiDAR, montiert über dem Verladegleis, IP65+-Gehäuse, scannt jeden Güterwagen, sobald er an der Verladeposition hält. Keine Kamera nötig, kein zweiter Sensor zu kalibrieren. Läuft durch Staub, Schnee, Nachtlicht und den Dampf, der von warmem Schüttgut aufsteigt. Ein Scan pro Waggon-Stopp, gespeichert auf lokalem Speicher mit pro-Waggon-UUID für den Audit-Trail. One LiDAR mounted above the loading track, IP65+ housing, scans every wagon as it stops at the loading position. No camera required, no second sensor to calibrate. Runs through dust, snow, night light and the steam that comes off warm bulk material. One scan per wagon stop, written to local storage with a per-wagon UUID for audit trail. One LiDAR mounted above the loading track, IP65+ housing, scans every wagon as it stops at the loading position. No camera required, no second sensor to calibrate. Runs through dust, snow, night light and the steam that comes off warm bulk material. One scan per wagon stop, written to local storage with a per-wagon UUID for audit trail. One LiDAR mounted above the loading track, IP65+ housing, scans every wagon as it stops at the loading position. No camera required, no second sensor to calibrate. Runs through dust, snow, night light and the steam that comes off warm bulk material. One scan per wagon stop, written to local storage with a per-wagon UUID for audit trail. One LiDAR mounted above the loading track, IP65+ housing, scans every wagon as it stops at the loading position. No camera required, no second sensor to calibrate. Runs through dust, snow, night light and the steam that comes off warm bulk material. One scan per wagon stop, written to local storage with a per-wagon UUID for audit trail. Um LiDAR montado sobre o trilho de carregamento, com carcaça IP65+, faz a varredura de cada vagão quando ele para na posição de carga. Sem câmera, sem segundo sensor para calibrar. Funciona através de poeira, neve, iluminação noturna e o vapor que sai do material a granel quente. Uma varredura por parada de vagão, gravada em armazenamento local com UUID por vagão para trilha de auditoria.

2 · Six-stage geometric pipeline2 · Sechsstufige geometrische Pipeline2 · Six-stage geometric pipeline2 · Six-stage geometric pipeline2 · Six-stage geometric pipeline2 · Six-stage geometric pipeline2 · Pipeline geométrico de seis estágios

Pure NumPy and scipy in the Python prototype, ports straight to C++/C# for the plant controller. Six stateless stages: ground crop, deck-Z estimation, height-band masking, lid clustering, wagon-bounds, tracker state machine. Runs in under two seconds on commodity hardware. No GPU required, no model retraining when the wagon class changes — calibration lives in config.yaml. Reines NumPy und scipy im Python-Prototyp, portiert direkt nach C++/C# für den Anlagencontroller. Sechs zustandslose Stufen: Ground-Crop, Deck-Z-Schätzung, Höhenband-Maskierung, Deckel-Clustering, Waggon-Bounds, Tracker-Zustandsautomat. Läuft in unter zwei Sekunden auf Standard-Hardware. Keine GPU nötig, kein Modell-Retraining bei Waggon-Klassen-Wechsel — Kalibrierung liegt in config.yaml. Pure NumPy and scipy in the Python prototype, ports straight to C++/C# for the plant controller. Six stateless stages: ground crop, deck-Z estimation, height-band masking, lid clustering, wagon-bounds, tracker state machine. Runs in under two seconds on commodity hardware. No GPU required, no model retraining when the wagon class changes — calibration lives in config.yaml. Pure NumPy and scipy in the Python prototype, ports straight to C++/C# for the plant controller. Six stateless stages: ground crop, deck-Z estimation, height-band masking, lid clustering, wagon-bounds, tracker state machine. Runs in under two seconds on commodity hardware. No GPU required, no model retraining when the wagon class changes — calibration lives in config.yaml. Pure NumPy and scipy in the Python prototype, ports straight to C++/C# for the plant controller. Six stateless stages: ground crop, deck-Z estimation, height-band masking, lid clustering, wagon-bounds, tracker state machine. Runs in under two seconds on commodity hardware. No GPU required, no model retraining when the wagon class changes — calibration lives in config.yaml. Pure NumPy and scipy in the Python prototype, ports straight to C++/C# for the plant controller. Six stateless stages: ground crop, deck-Z estimation, height-band masking, lid clustering, wagon-bounds, tracker state machine. Runs in under two seconds on commodity hardware. No GPU required, no model retraining when the wagon class changes — calibration lives in config.yaml. NumPy e scipy puros no protótipo Python, portáveis diretamente para C++/C# no controlador da planta. Seis estágios sem estado: corte do solo, estimativa de Z do deck, mascaramento por faixa de altura, clusterização das tampas, contornos do vagão, máquina de estados do rastreador. Executa em menos de dois segundos em hardware comum. Sem GPU, sem retreinamento de modelo quando muda a classe do vagão — a calibração vive no config.yaml.

3 · ERP / MES integration3 · ERP- / MES-Anbindung3 · ERP / MES integration3 · ERP / MES integration3 · ERP / MES integration3 · ERP / MES integration3 · Integração ERP / MES

Wagon ID, lid coordinates and material assignment write back over OPC UA or REST — same wagon stop, before the next train pulls in. The loading control system reads real wagon positions instead of operator clipboard estimates. Misroutes get flagged before the lid opens; the ERP material log matches the yard state by the end of the shift. Waggon-ID, Deckel-Koordinaten und Material-Zuordnung schreiben zurück per OPC UA oder REST — gleicher Waggon-Stopp, bevor der nächste Zug einfährt. Die Verladesteuerung liest echte Waggonpositionen statt Bediener-Schätzungen vom Klemmbrett. Fehl-Routings werden markiert, bevor der Deckel öffnet; das ERP-Material-Log passt zum Hof-Zustand am Schichtende. Wagon ID, lid coordinates and material assignment write back over OPC UA or REST — same wagon stop, before the next train pulls in. The loading control system reads real wagon positions instead of operator clipboard estimates. Misroutes get flagged before the lid opens; the ERP material log matches the yard state by the end of the shift. Wagon ID, lid coordinates and material assignment write back over OPC UA or REST — same wagon stop, before the next train pulls in. The loading control system reads real wagon positions instead of operator clipboard estimates. Misroutes get flagged before the lid opens; the ERP material log matches the yard state by the end of the shift. Wagon ID, lid coordinates and material assignment write back over OPC UA or REST — same wagon stop, before the next train pulls in. The loading control system reads real wagon positions instead of operator clipboard estimates. Misroutes get flagged before the lid opens; the ERP material log matches the yard state by the end of the shift. Wagon ID, lid coordinates and material assignment write back over OPC UA or REST — same wagon stop, before the next train pulls in. The loading control system reads real wagon positions instead of operator clipboard estimates. Misroutes get flagged before the lid opens; the ERP material log matches the yard state by the end of the shift. ID do vagão, coordenadas das tampas e atribuição de material são gravados via OPC UA ou REST — na mesma parada do vagão, antes do próximo trem entrar. O sistema de controle de carga lê posições reais em vez das estimativas do operador na prancheta. Erros de roteamento são sinalizados antes da tampa abrir; o registro de material no ERP bate com o estado do pátio no final do turno.

Pipeline architecture · #how-it-worksPipeline-Architektur · #how-it-worksPipeline architecture · #how-it-worksPipeline architecture · #how-it-worksPipeline architecture · #how-it-worksPipeline architecture · #how-it-worksArquitetura do pipeline · #how-it-works

How the pipeline works — concretely. Wie die Pipeline arbeitet — konkret. How the pipeline works — concretely. How the pipeline works — concretely. How the pipeline works — concretely. How the pipeline works — concretely. Como o pipeline funciona — concretamente.

The architecture is boring on purpose. Three loosely coupled stages, each one independently testable, each one swappable when the rail operator changes wagon class or relocates the loading position. Built on the same stack we use across all our perception work: NumPy, scipy, PCL, Open3D. Die Architektur ist absichtlich langweilig. Drei lose gekoppelte Stufen, jede einzeln testbar, jede einzeln tauschbar, wenn der Bahnbetreiber die Waggon-Klasse ändert oder die Verladeposition verschiebt. Gebaut auf demselben Stack, den wir für unsere gesamte Wahrnehmungs-Arbeit nutzen: NumPy, scipy, PCL, Open3D. The architecture is boring on purpose. Three loosely coupled stages, each one independently testable, each one swappable when the rail operator changes wagon class or relocates the loading position. Built on the same stack we use across all our perception work: NumPy, scipy, PCL, Open3D. The architecture is boring on purpose. Three loosely coupled stages, each one independently testable, each one swappable when the rail operator changes wagon class or relocates the loading position. Built on the same stack we use across all our perception work: NumPy, scipy, PCL, Open3D. The architecture is boring on purpose. Three loosely coupled stages, each one independently testable, each one swappable when the rail operator changes wagon class or relocates the loading position. Built on the same stack we use across all our perception work: NumPy, scipy, PCL, Open3D. The architecture is boring on purpose. Three loosely coupled stages, each one independently testable, each one swappable when the rail operator changes wagon class or relocates the loading position. Built on the same stack we use across all our perception work: NumPy, scipy, PCL, Open3D. A arquitetura é chata de propósito. Três estágios fracamente acoplados, cada um testável de forma independente, cada um substituível quando o operador ferroviário troca a classe de vagão ou realoca a posição de carregamento. Construído na mesma pilha que usamos em todos os nossos trabalhos de percepção: NumPy, scipy, PCL, Open3D.

Scan captureScan-AufnahmeScan captureScan captureScan captureScan captureCaptura de varredura

A single LAS file per wagon stop — typically a million-plus points covering the wagon deck, the lids and a strip of track on either side. The LiDAR captures the scan as the wagon comes to rest at the loading position. Raw point cloud writes to local storage with a per-wagon UUID for audit trail and re-labelling. Eine einzige LAS-Datei pro Waggon-Stopp — typisch über eine Million Punkte über das Waggon-Deck, die Deckel und einen Streifen Gleis links und rechts. Der LiDAR nimmt den Scan auf, sobald der Waggon an der Verladeposition zum Stehen kommt. Die Rohpunktwolke schreibt auf lokalen Speicher mit pro-Waggon-UUID für Audit-Trail und Re-Labelling. A single LAS file per wagon stop — typically a million-plus points covering the wagon deck, the lids and a strip of track on either side. The LiDAR captures the scan as the wagon comes to rest at the loading position. Raw point cloud writes to local storage with a per-wagon UUID for audit trail and re-labelling. A single LAS file per wagon stop — typically a million-plus points covering the wagon deck, the lids and a strip of track on either side. The LiDAR captures the scan as the wagon comes to rest at the loading position. Raw point cloud writes to local storage with a per-wagon UUID for audit trail and re-labelling. A single LAS file per wagon stop — typically a million-plus points covering the wagon deck, the lids and a strip of track on either side. The LiDAR captures the scan as the wagon comes to rest at the loading position. Raw point cloud writes to local storage with a per-wagon UUID for audit trail and re-labelling. A single LAS file per wagon stop — typically a million-plus points covering the wagon deck, the lids and a strip of track on either side. The LiDAR captures the scan as the wagon comes to rest at the loading position. Raw point cloud writes to local storage with a per-wagon UUID for audit trail and re-labelling. Um único arquivo LAS por parada de vagão — tipicamente mais de um milhão de pontos cobrindo o deck do vagão, as tampas e uma faixa de trilho de cada lado. O LiDAR captura a varredura assim que o vagão para na posição de carregamento. A nuvem de pontos bruta é gravada em armazenamento local com UUID por vagão para trilha de auditoria e rerrotulagem.

Six-stage geometric pipelineSechsstufige geometrische PipelineSix-stage geometric pipelineSix-stage geometric pipelineSix-stage geometric pipelineSix-stage geometric pipelinePipeline geométrico de seis estágios

Ground crop removes the track and ballast. Deck-Z estimation locates the wagon-deck plane. Height-band masking isolates the lid layer. Lid clustering returns the six centerpoints. Wagon-bounds gives the 3D box. The tracker state machine assigns a sequence number and flags the leading Roboterwagen if it is in frame. Mean centroid error stays under 5 cm against hand-labelled ground truth. Ground-Crop entfernt Gleis und Schotter. Deck-Z-Schätzung lokalisiert die Waggon-Deck-Ebene. Höhenband-Maskierung isoliert die Deckel-Schicht. Deckel-Clustering liefert die sechs Mittelpunkte. Waggon-Bounds gibt die 3D-Box. Der Tracker-Zustandsautomat vergibt die Sequenznummer und markiert den vorausfahrenden Roboterwagen, falls im Bild. Der mittlere Mittelpunkts-Fehler bleibt unter 5 cm gegen handgelabelte Ground Truth. Ground crop removes the track and ballast. Deck-Z estimation locates the wagon-deck plane. Height-band masking isolates the lid layer. Lid clustering returns the six centerpoints. Wagon-bounds gives the 3D box. The tracker state machine assigns a sequence number and flags the leading Roboterwagen if it is in frame. Mean centroid error stays under 5 cm against hand-labelled ground truth. Ground crop removes the track and ballast. Deck-Z estimation locates the wagon-deck plane. Height-band masking isolates the lid layer. Lid clustering returns the six centerpoints. Wagon-bounds gives the 3D box. The tracker state machine assigns a sequence number and flags the leading Roboterwagen if it is in frame. Mean centroid error stays under 5 cm against hand-labelled ground truth. Ground crop removes the track and ballast. Deck-Z estimation locates the wagon-deck plane. Height-band masking isolates the lid layer. Lid clustering returns the six centerpoints. Wagon-bounds gives the 3D box. The tracker state machine assigns a sequence number and flags the leading Roboterwagen if it is in frame. Mean centroid error stays under 5 cm against hand-labelled ground truth. Ground crop removes the track and ballast. Deck-Z estimation locates the wagon-deck plane. Height-band masking isolates the lid layer. Lid clustering returns the six centerpoints. Wagon-bounds gives the 3D box. The tracker state machine assigns a sequence number and flags the leading Roboterwagen if it is in frame. Mean centroid error stays under 5 cm against hand-labelled ground truth. O corte do solo remove o trilho e o lastro. A estimativa de Z do deck localiza o plano do deck do vagão. O mascaramento por faixa de altura isola a camada das tampas. A clusterização das tampas retorna os seis centros. Os contornos do vagão dão a caixa 3D. A máquina de estados do rastreador atribui um número de sequência e sinaliza o vagão-robô de controle líder, se estiver no quadro. O erro médio de centroide fica abaixo de 5 cm em relação ao ground truth rotulado à mão.

Per-wagon record write-backRück-Schreibung pro WaggonPer-wagon record write-backPer-wagon record write-backPer-wagon record write-backPer-wagon record write-backGravação de registro por vagão

Output per wagon: JSON record with wagon ID, lid centerpoints, material assignment and audit images. Writes into the ERP, the MES or the loading-control system over OPC UA or REST. The Electron + Vue 3 + Three.js operator UI shows the live 3D point cloud, the train summary and a CSV export for the shift log. Ausgabe pro Waggon: JSON-Datensatz mit Waggon-ID, Deckel-Mittelpunkten, Material-Zuordnung und Audit-Bildern. Schreibt ins ERP, MES oder Verladesteuerungs-System per OPC UA oder REST. Die Bediener-Oberfläche in Electron + Vue 3 + Three.js zeigt die Live-3D-Punktwolke, die Zug-Übersicht und einen CSV-Export für das Schicht-Log. Output per wagon: JSON record with wagon ID, lid centerpoints, material assignment and audit images. Writes into the ERP, the MES or the loading-control system over OPC UA or REST. The Electron + Vue 3 + Three.js operator UI shows the live 3D point cloud, the train summary and a CSV export for the shift log. Output per wagon: JSON record with wagon ID, lid centerpoints, material assignment and audit images. Writes into the ERP, the MES or the loading-control system over OPC UA or REST. The Electron + Vue 3 + Three.js operator UI shows the live 3D point cloud, the train summary and a CSV export for the shift log. Output per wagon: JSON record with wagon ID, lid centerpoints, material assignment and audit images. Writes into the ERP, the MES or the loading-control system over OPC UA or REST. The Electron + Vue 3 + Three.js operator UI shows the live 3D point cloud, the train summary and a CSV export for the shift log. Output per wagon: JSON record with wagon ID, lid centerpoints, material assignment and audit images. Writes into the ERP, the MES or the loading-control system over OPC UA or REST. The Electron + Vue 3 + Three.js operator UI shows the live 3D point cloud, the train summary and a CSV export for the shift log. Saída por vagão: registro JSON com ID do vagão, centros das tampas, atribuição de material e imagens de auditoria. Grava no ERP, no MES ou no sistema de controle de carga via OPC UA ou REST. A interface de operador em Electron + Vue 3 + Three.js mostra a nuvem de pontos 3D ao vivo, o resumo do trem e uma exportação CSV para o log do turno.

All three stages run on an industrial PC at the loading station — Python prototype for development, C++/C# port for the final controller. No cloud dependency, no external API, no licence dial-home. The code is yours at handover. Alle drei Stufen laufen auf einem Industrie-PC an der Verladestation — Python-Prototyp für die Entwicklung, C++/C#-Portierung für den finalen Controller. Keine Cloud-Abhängigkeit, keine externe API, kein Lizenz-Heimruf. Der Code gehört Ihnen bei der Übergabe. All three stages run on an industrial PC at the loading station — Python prototype for development, C++/C# port for the final controller. No cloud dependency, no external API, no licence dial-home. The code is yours at handover. All three stages run on an industrial PC at the loading station — Python prototype for development, C++/C# port for the final controller. No cloud dependency, no external API, no licence dial-home. The code is yours at handover. All three stages run on an industrial PC at the loading station — Python prototype for development, C++/C# port for the final controller. No cloud dependency, no external API, no licence dial-home. The code is yours at handover. All three stages run on an industrial PC at the loading station — Python prototype for development, C++/C# port for the final controller. No cloud dependency, no external API, no licence dial-home. The code is yours at handover. Todos os três estágios rodam em um PC industrial na estação de carregamento — protótipo Python para desenvolvimento, porte C++/C# para o controlador final. Sem dependência de nuvem, sem API externa, sem licença que "telefona para casa". O código é seu na entrega.

What you getWas Sie bekommenWhat you getWhat you getWhat you getWhat you getO que você recebe

Three deliverables — from the same shipped pipeline. Drei Liefer-Ergebnisse — aus derselben produktiven Pipeline. Three deliverables — from the same shipped pipeline. Three deliverables — from the same shipped pipeline. Three deliverables — from the same shipped pipeline. Three deliverables — from the same shipped pipeline. Três entregáveis — do mesmo pipeline entregue.

Wagon position + identityWaggonposition + IdentitätWagon position + identityWagon position + identityWagon position + identityWagon position + identityPosição + identidade do vagão

Every wagon stop returns an exact 3D bounding box, the six lid centerpoints, a wagon sequence number and a Roboterwagen flag. Drops directly into the loading-control state machine so the next loading move is unambiguous. Same geometric output the Bahnwagon pipeline produces in production today. Jeder Waggon-Stopp liefert eine exakte 3D-Bounding-Box, die sechs Deckel-Mittelpunkte, eine Waggon-Sequenznummer und ein Roboterwagen-Flag. Geht direkt in den Zustandsautomaten der Verladesteuerung, so dass die nächste Verladebewegung eindeutig ist. Dieselbe geometrische Ausgabe, die die Bahnwagon-Pipeline heute produktiv erzeugt. Every wagon stop returns an exact 3D bounding box, the six lid centerpoints, a wagon sequence number and a Roboterwagen flag. Drops directly into the loading-control state machine so the next loading move is unambiguous. Same geometric output the Bahnwagon pipeline produces in production today. Every wagon stop returns an exact 3D bounding box, the six lid centerpoints, a wagon sequence number and a Roboterwagen flag. Drops directly into the loading-control state machine so the next loading move is unambiguous. Same geometric output the Bahnwagon pipeline produces in production today. Every wagon stop returns an exact 3D bounding box, the six lid centerpoints, a wagon sequence number and a Roboterwagen flag. Drops directly into the loading-control state machine so the next loading move is unambiguous. Same geometric output the Bahnwagon pipeline produces in production today. Every wagon stop returns an exact 3D bounding box, the six lid centerpoints, a wagon sequence number and a Roboterwagen flag. Drops directly into the loading-control state machine so the next loading move is unambiguous. Same geometric output the Bahnwagon pipeline produces in production today. Cada parada de vagão retorna uma caixa delimitadora 3D exata, os seis centros das tampas, um número de sequência do vagão e um indicador de vagão-robô de controle. Entra diretamente na máquina de estados do controle de carga, de modo que o próximo movimento é inequívoco. Mesma saída geométrica que o pipeline Bahnwagon produz em produção hoje.

Per-wagon material logMaterial-Log pro WaggonPer-wagon material logPer-wagon material logPer-wagon material logPer-wagon material logRegistro de material por vagão

Material type, tonnage and per-lid breakdown write into ERP/MES per wagon — same shift, before the train leaves. No more clipboard reconciliation, no more billing the wrong wagon. The yard-management system sees the actual loaded mass instead of the operator estimate. Material-Typ, Tonnage und Deckel-Aufschlüsselung schreiben pro Waggon ins ERP/MES — gleiche Schicht, bevor der Zug abfährt. Kein Klemmbrett-Abgleich mehr, keine Falsch-Abrechnung mehr. Das Hof-Management-System sieht die tatsächlich beladene Masse statt der Bediener-Schätzung. Material type, tonnage and per-lid breakdown write into ERP/MES per wagon — same shift, before the train leaves. No more clipboard reconciliation, no more billing the wrong wagon. The yard-management system sees the actual loaded mass instead of the operator estimate. Material type, tonnage and per-lid breakdown write into ERP/MES per wagon — same shift, before the train leaves. No more clipboard reconciliation, no more billing the wrong wagon. The yard-management system sees the actual loaded mass instead of the operator estimate. Material type, tonnage and per-lid breakdown write into ERP/MES per wagon — same shift, before the train leaves. No more clipboard reconciliation, no more billing the wrong wagon. The yard-management system sees the actual loaded mass instead of the operator estimate. Material type, tonnage and per-lid breakdown write into ERP/MES per wagon — same shift, before the train leaves. No more clipboard reconciliation, no more billing the wrong wagon. The yard-management system sees the actual loaded mass instead of the operator estimate. Tipo de material, tonelagem e detalhamento por tampa são gravados no ERP/MES por vagão — mesmo turno, antes de o trem partir. Chega de reconciliar prancheta, chega de faturar o vagão errado. O sistema de gestão de pátio vê a massa real carregada em vez da estimativa do operador.

Electron operator UIElectron-Bediener-OberflächeElectron operator UIElectron operator UIElectron operator UIElectron operator UIInterface do operador em Electron

Vue 3 + Three.js desktop app for the loading-station operator: live 3D point cloud, per-wagon summary, train list, CSV export, wagon-by-wagon audit. Already in production with our Bahnwagon customer — handed over with full source. Run it locally on the plant PC; no browser-cloud dependency. Vue 3 + Three.js Desktop-App für den Verladestations-Bediener: Live-3D-Punktwolke, Übersicht pro Waggon, Zug-Liste, CSV-Export, Waggon-für-Waggon-Audit. Bereits produktiv beim Bahnwagon-Kunden im Einsatz — übergeben mit vollem Quellcode. Läuft lokal auf dem Anlagen-PC; keine Browser-Cloud-Abhängigkeit. Vue 3 + Three.js desktop app for the loading-station operator: live 3D point cloud, per-wagon summary, train list, CSV export, wagon-by-wagon audit. Already in production with our Bahnwagon customer — handed over with full source. Run it locally on the plant PC; no browser-cloud dependency. Vue 3 + Three.js desktop app for the loading-station operator: live 3D point cloud, per-wagon summary, train list, CSV export, wagon-by-wagon audit. Already in production with our Bahnwagon customer — handed over with full source. Run it locally on the plant PC; no browser-cloud dependency. Vue 3 + Three.js desktop app for the loading-station operator: live 3D point cloud, per-wagon summary, train list, CSV export, wagon-by-wagon audit. Already in production with our Bahnwagon customer — handed over with full source. Run it locally on the plant PC; no browser-cloud dependency. Vue 3 + Three.js desktop app for the loading-station operator: live 3D point cloud, per-wagon summary, train list, CSV export, wagon-by-wagon audit. Already in production with our Bahnwagon customer — handed over with full source. Run it locally on the plant PC; no browser-cloud dependency. Aplicativo desktop em Vue 3 + Three.js para o operador da estação de carga: nuvem de pontos 3D ao vivo, resumo por vagão, lista do trem, exportação CSV e auditoria vagão a vagão. Já em produção com nosso cliente Bahnwagon — entregue com código-fonte completo. Roda localmente no PC da planta; sem dependência de nuvem no navegador.

Why customWarum CustomWhy customWhy customWhy customWhy customPor que customizado

Why a custom build — not an off-the-shelf product. Warum nach Maß — und nicht von der Stange. Why a custom build — not an off-the-shelf product. Why a custom build — not an off-the-shelf product. Why a custom build — not an off-the-shelf product. Why a custom build — not an off-the-shelf product. Por que um build customizado — não um produto de prateleira.

Every loading station has its own wagon class, its own track geometry, its own existing control system. A generic detector solves the generic case; your loading position is not the generic case. Jede Verladestation hat ihre eigene Waggon-Klasse, ihre eigene Gleisgeometrie, ihre eigene bestehende Steuerung. Ein generischer Detektor löst den generischen Fall; Ihre Verladeposition ist nicht der generische Fall. Every loading station has its own wagon class, its own track geometry, its own existing control system. A generic detector solves the generic case; your loading position is not the generic case. Every loading station has its own wagon class, its own track geometry, its own existing control system. A generic detector solves the generic case; your loading position is not the generic case. Every loading station has its own wagon class, its own track geometry, its own existing control system. A generic detector solves the generic case; your loading position is not the generic case. Every loading station has its own wagon class, its own track geometry, its own existing control system. A generic detector solves the generic case; your loading position is not the generic case. Cada estação de carregamento tem sua própria classe de vagão, sua própria geometria de trilho, seu próprio sistema de controle existente. Um detector genérico resolve o caso genérico; sua posição de carregamento não é o caso genérico.

Plant-specific lid geometry calibrationAnlagen-spezifische Deckel-Geometrie-KalibrierungPlant-specific lid geometry calibrationPlant-specific lid geometry calibrationPlant-specific lid geometry calibrationPlant-specific lid geometry calibrationCalibração de geometria de tampa específica da planta

Every wagon class is different — six lids, eight lids, end-loaders, top-loaders, with or without a leading Roboterwagen. We calibrate the geometric pipeline on your wagons at your loading position, and we re-calibrate when the rail operator rotates the fleet. Calibration constants live in config.yaml — no retraining, no GPU. Jede Waggon-Klasse ist anders — sechs Deckel, acht Deckel, Stirn-Lader, Oben-Lader, mit oder ohne vorausfahrenden Roboterwagen. Wir kalibrieren die geometrische Pipeline auf Ihren Waggons an Ihrer Verladeposition und re-kalibrieren, wenn der Bahnbetreiber die Flotte tauscht. Kalibrierungs-Konstanten liegen in config.yaml — kein Retraining, keine GPU. Every wagon class is different — six lids, eight lids, end-loaders, top-loaders, with or without a leading Roboterwagen. We calibrate the geometric pipeline on your wagons at your loading position, and we re-calibrate when the rail operator rotates the fleet. Calibration constants live in config.yaml — no retraining, no GPU. Every wagon class is different — six lids, eight lids, end-loaders, top-loaders, with or without a leading Roboterwagen. We calibrate the geometric pipeline on your wagons at your loading position, and we re-calibrate when the rail operator rotates the fleet. Calibration constants live in config.yaml — no retraining, no GPU. Every wagon class is different — six lids, eight lids, end-loaders, top-loaders, with or without a leading Roboterwagen. We calibrate the geometric pipeline on your wagons at your loading position, and we re-calibrate when the rail operator rotates the fleet. Calibration constants live in config.yaml — no retraining, no GPU. Every wagon class is different — six lids, eight lids, end-loaders, top-loaders, with or without a leading Roboterwagen. We calibrate the geometric pipeline on your wagons at your loading position, and we re-calibrate when the rail operator rotates the fleet. Calibration constants live in config.yaml — no retraining, no GPU. Cada classe de vagão é diferente — seis tampas, oito tampas, carregamento na traseira, carregamento pelo topo, com ou sem vagão-robô de controle líder. Calibramos o pipeline geométrico nos seus vagões na sua posição de carregamento, e recalibramos quando o operador ferroviário troca a frota. As constantes de calibração vivem no config.yaml — sem retreinamento, sem GPU.

Payload-agnostic pipelineSchüttgut-agnostische PipelinePayload-agnostic pipelinePayload-agnostic pipelinePayload-agnostic pipelinePayload-agnostic pipelinePipeline agnóstico ao material

The same geometric approach works on rock, salt, scrap and grain — only the lid clustering tolerances change. No ML retraining required when the material switches; the pipeline cares about wagon geometry, not what is inside the lid. This is the property that made the Bahnwagon project portable across plants. Derselbe geometrische Ansatz funktioniert auf Stein, Salz, Schrott und Getreide — nur die Toleranzen im Deckel-Clustering ändern sich. Kein ML-Retraining bei Material-Wechsel; die Pipeline kümmert sich um Waggon-Geometrie, nicht um den Inhalt des Deckels. Genau diese Eigenschaft macht das Bahnwagon-Projekt anlagen-übergreifend portabel. The same geometric approach works on rock, salt, scrap and grain — only the lid clustering tolerances change. No ML retraining required when the material switches; the pipeline cares about wagon geometry, not what is inside the lid. This is the property that made the Bahnwagon project portable across plants. The same geometric approach works on rock, salt, scrap and grain — only the lid clustering tolerances change. No ML retraining required when the material switches; the pipeline cares about wagon geometry, not what is inside the lid. This is the property that made the Bahnwagon project portable across plants. The same geometric approach works on rock, salt, scrap and grain — only the lid clustering tolerances change. No ML retraining required when the material switches; the pipeline cares about wagon geometry, not what is inside the lid. This is the property that made the Bahnwagon project portable across plants. The same geometric approach works on rock, salt, scrap and grain — only the lid clustering tolerances change. No ML retraining required when the material switches; the pipeline cares about wagon geometry, not what is inside the lid. This is the property that made the Bahnwagon project portable across plants. A mesma abordagem geométrica funciona com rocha, sal, sucata e grãos — só mudam as tolerâncias de clusterização das tampas. Sem retreinamento de ML quando muda o material; o pipeline se importa com a geometria do vagão, não com o que está dentro da tampa. É essa propriedade que tornou o projeto Bahnwagon portável entre plantas.

Tracker as a separate state machineTracker als separater ZustandsautomatTracker as a separate state machineTracker as a separate state machineTracker as a separate state machineTracker as a separate state machineRastreador como máquina de estados separada

The geometric stages are stateless. State lives only in the tracker — the component that assigns wagon sequence numbers and detects the leading Roboterwagen. Clean boundary, easy to integrate with your existing yard-management state, easy to swap when the loading control software changes. Die geometrischen Stufen sind zustandslos. Zustand liegt nur im Tracker — der Komponente, die Waggon-Sequenznummern vergibt und den vorausfahrenden Roboterwagen erkennt. Saubere Grenze, einfache Integration in Ihren bestehenden Hof-Management-Zustand, einfach tauschbar, wenn sich die Verladesteuerungs-Software ändert. The geometric stages are stateless. State lives only in the tracker — the component that assigns wagon sequence numbers and detects the leading Roboterwagen. Clean boundary, easy to integrate with your existing yard-management state, easy to swap when the loading control software changes. The geometric stages are stateless. State lives only in the tracker — the component that assigns wagon sequence numbers and detects the leading Roboterwagen. Clean boundary, easy to integrate with your existing yard-management state, easy to swap when the loading control software changes. The geometric stages are stateless. State lives only in the tracker — the component that assigns wagon sequence numbers and detects the leading Roboterwagen. Clean boundary, easy to integrate with your existing yard-management state, easy to swap when the loading control software changes. The geometric stages are stateless. State lives only in the tracker — the component that assigns wagon sequence numbers and detects the leading Roboterwagen. Clean boundary, easy to integrate with your existing yard-management state, easy to swap when the loading control software changes. Os estágios geométricos são sem estado. O estado vive apenas no rastreador — o componente que atribui números de sequência e detecta o vagão-robô de controle líder. Fronteira limpa, fácil de integrar com o seu estado existente de gestão de pátio, fácil de trocar quando muda o software de controle de carga.

IP ownership + handoverIP-Eigentum + ÜbergabeIP ownership + handoverIP ownership + handoverIP ownership + handoverIP ownership + handoverPropriedade da IP + entrega

You own the Python prototype, the C++/C# port, the calibration files and the Electron operator UI at handover. We document the system, train your team and walk away clean. No black box, no monthly per-wagon licence, no service contract you cannot exit. See our FAQs on IP and engagement model for the standard terms. Sie besitzen den Python-Prototyp, die C++/C#-Portierung, die Kalibrierungs-Dateien und die Electron-Bediener-Oberfläche nach der Übergabe. Wir dokumentieren das System, schulen Ihr Team und gehen sauber raus. Keine Black Box, keine monatliche Pro-Waggon-Lizenz, kein Servicevertrag, aus dem Sie nicht rauskommen. Standard-Bedingungen siehe unsere FAQs zu IP und Zusammenarbeits-Modell. You own the Python prototype, the C++/C# port, the calibration files and the Electron operator UI at handover. We document the system, train your team and walk away clean. No black box, no monthly per-wagon licence, no service contract you cannot exit. See our FAQs on IP and engagement model for the standard terms. You own the Python prototype, the C++/C# port, the calibration files and the Electron operator UI at handover. We document the system, train your team and walk away clean. No black box, no monthly per-wagon licence, no service contract you cannot exit. See our FAQs on IP and engagement model for the standard terms. You own the Python prototype, the C++/C# port, the calibration files and the Electron operator UI at handover. We document the system, train your team and walk away clean. No black box, no monthly per-wagon licence, no service contract you cannot exit. See our FAQs on IP and engagement model for the standard terms. You own the Python prototype, the C++/C# port, the calibration files and the Electron operator UI at handover. We document the system, train your team and walk away clean. No black box, no monthly per-wagon licence, no service contract you cannot exit. See our FAQs on IP and engagement model for the standard terms. Você recebe a propriedade do protótipo em Python, do porte C++/C#, dos arquivos de calibração e da interface do operador em Electron na entrega. Documentamos o sistema, treinamos sua equipe e saímos limpos. Sem caixa-preta, sem licença mensal por vagão, sem contrato de serviço do qual você não possa sair. Veja nosso FAQ sobre IP e modelo de contrato para os termos padrão.

FAQ

Questions about railcar detection AI. Fragen zur Waggon-Erkennung KI. Questions about railcar detection AI. Questions about railcar detection AI. Questions about railcar detection AI. Questions about railcar detection AI. Perguntas sobre IA de detecção de vagões ferroviários.

The engagement-model questions we hear from every loading-station customer considering a custom perception build. Need something more specific to your wagon class? Ask us. Die Fragen zum Zusammenarbeits-Modell, die wir von jedem Verladestations-Kunden hören, der einen Custom-Perception-Build erwägt. Brauchen Sie etwas Spezifischeres zu Ihrer Waggon-Klasse? Sprechen Sie uns an. The engagement-model questions we hear from every loading-station customer considering a custom perception build. Need something more specific to your wagon class? Ask us. The engagement-model questions we hear from every loading-station customer considering a custom perception build. Need something more specific to your wagon class? Ask us. The engagement-model questions we hear from every loading-station customer considering a custom perception build. Need something more specific to your wagon class? Ask us. The engagement-model questions we hear from every loading-station customer considering a custom perception build. Need something more specific to your wagon class? Ask us. As perguntas de modelo de contrato que ouvimos de cada cliente de estação de carregamento considerando um build customizado de percepção. Precisa de algo mais específico para a sua classe de vagão? Pergunte.

Industrial Perception AIIndustrielle ObjekterkennungIndustrial Perception AIIndustrial Perception AIIndustrial Perception AIIndustrial Perception AIIndustrial Perception AI

What does custom perception AI software cost?Was kostet maßgeschneiderte Wahrnehmungs-Software?What does custom perception AI software cost?What does custom perception AI software cost?What does custom perception AI software cost?What does custom perception AI software cost?Quanto custa um software de perception AI sob medida? ＋

 We don't publish list prices because every project is scoped against your data and your decision logic — but we work to three predictable tiers.

Discovery & Assessment — a 1–3 day workshop, on-site or remote, fixed price in the low-four-figure range. You receive a written feasibility note, a recommended next step and (if applicable) a fixed-price quote for the follow-on project. Useful even if you don't then go ahead — many customers use the note to evaluate two or three vendors.

Custom Pipeline / Tool — a 4–12 week project delivering a working perception pipeline (parser + algorithm + dashboard or OPC UA integration). Fixed scope, fixed price, you own the code at handover. Typical project size is in the mid-five to low-six-figure range depending on data volume, sensor count and integration depth.

Long-term Partnership — a monthly retainer for ongoing development and on-call support. Sized to the team capacity you need (typically 0.5–2 FTE equivalent). Quarterly road-mapping included.

All three tiers include source code, written documentation and a clean handover — no licence dependence on us. See our Industrial Perception AI service for the full engagement model.
Wir veröffentlichen keine Listenpreise, weil jedes Projekt auf Ihre Daten und Ihre Entscheidungs-Logik zugeschnitten wird — aber wir arbeiten in drei berechenbaren Tiers.

Discovery & Assessment — ein 1–3-tägiger Workshop, vor Ort oder remote, Festpreis im unteren vierstelligen Bereich. Sie erhalten eine schriftliche Machbarkeits-Note, einen empfohlenen nächsten Schritt und ggf. ein Festpreis-Angebot für das Folge-Projekt. Lohnt sich auch ohne Folge-Auftrag — viele Kunden nutzen die Note, um zwei oder drei Anbieter zu vergleichen.

Custom Pipeline / Tool — ein 4–12-Wochen-Projekt, das eine fertige Wahrnehmungs-Pipeline liefert (Parser + Algorithmus + Dashboard oder OPC-UA-Anbindung). Fester Scope, Festpreis, Code-Übergabe am Ende. Übliche Projektgröße liegt im mittleren fünf- bis unteren sechsstelligen Bereich, je nach Datenvolumen, Sensor-Anzahl und Integrations-Tiefe.

Langfristige Partnerschaft — monatlicher Retainer für laufende Entwicklung und On-Call-Support. Dimensioniert nach benötigter Team-Kapazität (typisch 0,5–2 FTE-Äquivalente). Quartalsweise Roadmap inklusive.

Alle drei Tiers enthalten Quellcode, schriftliche Dokumentation und saubere Übergabe — keine Lizenz-Abhängigkeit von uns. Vollständiges Zusammenarbeits-Modell siehe unsere Industrielle Objekterkennung.
We don't publish list prices because every project is scoped against your data and your decision logic — but we work to three predictable tiers.

Discovery & Assessment — a 1–3 day workshop, on-site or remote, fixed price in the low-four-figure range. You receive a written feasibility note, a recommended next step and (if applicable) a fixed-price quote for the follow-on project. Useful even if you don't then go ahead — many customers use the note to evaluate two or three vendors.

Custom Pipeline / Tool — a 4–12 week project delivering a working perception pipeline (parser + algorithm + dashboard or OPC UA integration). Fixed scope, fixed price, you own the code at handover. Typical project size is in the mid-five to low-six-figure range depending on data volume, sensor count and integration depth.

Long-term Partnership — a monthly retainer for ongoing development and on-call support. Sized to the team capacity you need (typically 0.5–2 FTE equivalent). Quarterly road-mapping included.

All three tiers include source code, written documentation and a clean handover — no licence dependence on us. See our Industrial Perception AI service for the full engagement model.
We don't publish list prices because every project is scoped against your data and your decision logic — but we work to three predictable tiers.

Discovery & Assessment — a 1–3 day workshop, on-site or remote, fixed price in the low-four-figure range. You receive a written feasibility note, a recommended next step and (if applicable) a fixed-price quote for the follow-on project. Useful even if you don't then go ahead — many customers use the note to evaluate two or three vendors.

Custom Pipeline / Tool — a 4–12 week project delivering a working perception pipeline (parser + algorithm + dashboard or OPC UA integration). Fixed scope, fixed price, you own the code at handover. Typical project size is in the mid-five to low-six-figure range depending on data volume, sensor count and integration depth.

Long-term Partnership — a monthly retainer for ongoing development and on-call support. Sized to the team capacity you need (typically 0.5–2 FTE equivalent). Quarterly road-mapping included.

All three tiers include source code, written documentation and a clean handover — no licence dependence on us. See our Industrial Perception AI service for the full engagement model.
We don't publish list prices because every project is scoped against your data and your decision logic — but we work to three predictable tiers.

Discovery & Assessment — a 1–3 day workshop, on-site or remote, fixed price in the low-four-figure range. You receive a written feasibility note, a recommended next step and (if applicable) a fixed-price quote for the follow-on project. Useful even if you don't then go ahead — many customers use the note to evaluate two or three vendors.

Custom Pipeline / Tool — a 4–12 week project delivering a working perception pipeline (parser + algorithm + dashboard or OPC UA integration). Fixed scope, fixed price, you own the code at handover. Typical project size is in the mid-five to low-six-figure range depending on data volume, sensor count and integration depth.

Long-term Partnership — a monthly retainer for ongoing development and on-call support. Sized to the team capacity you need (typically 0.5–2 FTE equivalent). Quarterly road-mapping included.

All three tiers include source code, written documentation and a clean handover — no licence dependence on us. See our Industrial Perception AI service for the full engagement model.
We don't publish list prices because every project is scoped against your data and your decision logic — but we work to three predictable tiers.

Discovery & Assessment — a 1–3 day workshop, on-site or remote, fixed price in the low-four-figure range. You receive a written feasibility note, a recommended next step and (if applicable) a fixed-price quote for the follow-on project. Useful even if you don't then go ahead — many customers use the note to evaluate two or three vendors.

Custom Pipeline / Tool — a 4–12 week project delivering a working perception pipeline (parser + algorithm + dashboard or OPC UA integration). Fixed scope, fixed price, you own the code at handover. Typical project size is in the mid-five to low-six-figure range depending on data volume, sensor count and integration depth.

Long-term Partnership — a monthly retainer for ongoing development and on-call support. Sized to the team capacity you need (typically 0.5–2 FTE equivalent). Quarterly road-mapping included.

All three tiers include source code, written documentation and a clean handover — no licence dependence on us. See our Industrial Perception AI service for the full engagement model.
Não divulgamos preços de tabela porque cada projeto é dimensionado conforme os seus dados e a sua lógica de decisão — mas trabalhamos em três tiers previsíveis.

Discovery & Assessment — um workshop de 1 a 3 dias, presencial ou remoto, preço fechado na faixa dos quatro dígitos baixos. Você recebe uma nota escrita de viabilidade, um próximo passo recomendado e (se aplicável) uma proposta de preço fechado para o projeto subsequente. Útil mesmo que você não avance depois — muitos clientes usam a nota para avaliar dois ou três fornecedores.

Custom Pipeline / Tool — um projeto de 4 a 12 semanas que entrega uma pipeline de percepção pronta (parser + algoritmo + painel ou integração OPC UA). Escopo fechado, preço fechado, você é dono do código na entrega. O tamanho típico do projeto fica entre a faixa média de cinco dígitos e a faixa baixa de seis dígitos, dependendo do volume de dados, número de sensores e profundidade da integração.

Long-term Partnership — um retainer mensal para desenvolvimento contínuo e suporte on-call. Dimensionado conforme a capacidade de equipe necessária (tipicamente 0,5 a 2 equivalentes de FTE). Road-mapping trimestral incluído.

Todos os três tiers incluem código-fonte, documentação escrita e uma entrega limpa — sem dependência de licença nossa. Consulte o nosso serviço Industrial Perception AI para o modelo completo de colaboração.

How long does it take to build a custom perception pipeline?Wie lange dauert eine maßgeschneiderte Wahrnehmungs-Pipeline?How long does it take to build a custom perception pipeline?How long does it take to build a custom perception pipeline?How long does it take to build a custom perception pipeline?How long does it take to build a custom perception pipeline?Quanto tempo leva para construir uma pipeline de percepção sob medida? ＋

 From signed contract to a working tool in production, expect 4–12 weeks for a standard custom-pipeline engagement. The spread is driven by three factors.

Data readiness. If you already have labelled data or representative recordings, we start training in week 1. If we need to set up data collection, add a sensor, or label from scratch, the front end adds 1–3 weeks.

Integration depth. A standalone dashboard with REST output is the fastest path — typically 4–6 weeks. OPC UA integration into a running control system, with interlocks, factory-acceptance tests and a customer change-management process, is closer to 8–12 weeks.

Sensor count and site complexity. One LiDAR + one camera in a clean indoor environment is faster than a six-sensor outdoor portal with dust, snow and dynamic lighting. We scope the difference up-front in the Discovery workshop.

For a faster first signal we recommend the Discovery & Assessment tier — 1–3 days, fixed price, written feasibility note within a week. See Industrial Perception AI for the full model.
Vom unterzeichneten Vertrag bis zum produktiven Tool rechnen Sie mit 4–12 Wochen für ein Standard-Custom-Pipeline-Projekt. Die Spanne wird von drei Faktoren bestimmt.

Datenlage. Wenn Sie bereits gelabelte Daten oder repräsentative Aufnahmen haben, starten wir in Woche 1 mit dem Training. Müssen wir Datenerfassung aufsetzen, einen Sensor ergänzen oder von Null labeln, kommt vorne 1–3 Wochen dazu.

Integrations-Tiefe. Ein eigenständiges Dashboard mit REST-Schnittstelle ist der schnellste Weg — typisch 4–6 Wochen. OPC-UA-Anbindung in eine laufende Leittechnik, mit Verriegelungen, Werks-Abnahme und kundenseitigem Change-Management, eher 8–12 Wochen.

Sensor-Anzahl und Anlagen-Komplexität. Ein LiDAR + eine Kamera in einer sauberen Innen-Umgebung geht schneller als ein 6-Sensor-Portal im Freien mit Staub, Schnee und wechselndem Licht. Den Unterschied schneiden wir vorab im Discovery-Workshop sauber zu.

Für ein schnelleres erstes Signal empfehlen wir das Discovery & Assessment-Tier — 1–3 Tage, Festpreis, schriftliche Machbarkeits-Note innerhalb einer Woche. Vollständiges Modell siehe Industrielle Objekterkennung.
From signed contract to a working tool in production, expect 4–12 weeks for a standard custom-pipeline engagement. The spread is driven by three factors.

Data readiness. If you already have labelled data or representative recordings, we start training in week 1. If we need to set up data collection, add a sensor, or label from scratch, the front end adds 1–3 weeks.

Integration depth. A standalone dashboard with REST output is the fastest path — typically 4–6 weeks. OPC UA integration into a running control system, with interlocks, factory-acceptance tests and a customer change-management process, is closer to 8–12 weeks.

Sensor count and site complexity. One LiDAR + one camera in a clean indoor environment is faster than a six-sensor outdoor portal with dust, snow and dynamic lighting. We scope the difference up-front in the Discovery workshop.

For a faster first signal we recommend the Discovery & Assessment tier — 1–3 days, fixed price, written feasibility note within a week. See Industrial Perception AI for the full model.
From signed contract to a working tool in production, expect 4–12 weeks for a standard custom-pipeline engagement. The spread is driven by three factors.

Data readiness. If you already have labelled data or representative recordings, we start training in week 1. If we need to set up data collection, add a sensor, or label from scratch, the front end adds 1–3 weeks.

Integration depth. A standalone dashboard with REST output is the fastest path — typically 4–6 weeks. OPC UA integration into a running control system, with interlocks, factory-acceptance tests and a customer change-management process, is closer to 8–12 weeks.

Sensor count and site complexity. One LiDAR + one camera in a clean indoor environment is faster than a six-sensor outdoor portal with dust, snow and dynamic lighting. We scope the difference up-front in the Discovery workshop.

For a faster first signal we recommend the Discovery & Assessment tier — 1–3 days, fixed price, written feasibility note within a week. See Industrial Perception AI for the full model.
From signed contract to a working tool in production, expect 4–12 weeks for a standard custom-pipeline engagement. The spread is driven by three factors.

Data readiness. If you already have labelled data or representative recordings, we start training in week 1. If we need to set up data collection, add a sensor, or label from scratch, the front end adds 1–3 weeks.

Integration depth. A standalone dashboard with REST output is the fastest path — typically 4–6 weeks. OPC UA integration into a running control system, with interlocks, factory-acceptance tests and a customer change-management process, is closer to 8–12 weeks.

Sensor count and site complexity. One LiDAR + one camera in a clean indoor environment is faster than a six-sensor outdoor portal with dust, snow and dynamic lighting. We scope the difference up-front in the Discovery workshop.

For a faster first signal we recommend the Discovery & Assessment tier — 1–3 days, fixed price, written feasibility note within a week. See Industrial Perception AI for the full model.
From signed contract to a working tool in production, expect 4–12 weeks for a standard custom-pipeline engagement. The spread is driven by three factors.

Data readiness. If you already have labelled data or representative recordings, we start training in week 1. If we need to set up data collection, add a sensor, or label from scratch, the front end adds 1–3 weeks.

Integration depth. A standalone dashboard with REST output is the fastest path — typically 4–6 weeks. OPC UA integration into a running control system, with interlocks, factory-acceptance tests and a customer change-management process, is closer to 8–12 weeks.

Sensor count and site complexity. One LiDAR + one camera in a clean indoor environment is faster than a six-sensor outdoor portal with dust, snow and dynamic lighting. We scope the difference up-front in the Discovery workshop.

For a faster first signal we recommend the Discovery & Assessment tier — 1–3 days, fixed price, written feasibility note within a week. See Industrial Perception AI for the full model.
Do contrato assinado à ferramenta funcionando em produção, espere 4 a 12 semanas para um projeto padrão de pipeline sob medida. A variação é determinada por três fatores.

Prontidão dos dados. Se você já tem dados rotulados ou gravações representativas, começamos o treinamento na semana 1. Se precisarmos configurar a coleta de dados, adicionar um sensor ou rotular do zero, o front end acrescenta de 1 a 3 semanas.

Profundidade de integração. Um painel autônomo com saída REST é o caminho mais rápido — tipicamente de 4 a 6 semanas. Integração OPC UA em um sistema de controle em operação, com intertravamentos, testes de aceitação de fábrica e um processo de gestão de mudanças do cliente, fica mais próxima de 8 a 12 semanas.

Número de sensores e complexidade do site. Um LiDAR + uma câmera em um ambiente interno limpo é mais rápido do que um pórtico externo com seis sensores em meio a poeira, neve e iluminação dinâmica. Nós dimensionamos essa diferença antecipadamente no workshop Discovery.

Para um primeiro sinal mais rápido recomendamos o tier Discovery & Assessment — 1 a 3 dias, preço fechado, nota escrita de viabilidade em até uma semana. Consulte Industrial Perception AI para o modelo completo.

Who owns the code and IP we pay you to build?Wem gehören Code und IP, die wir bei Ihnen beauftragen?Who owns the code and IP we pay you to build?Who owns the code and IP we pay you to build?Who owns the code and IP we pay you to build?Who owns the code and IP we pay you to build?Quem é dono do código e da PI que pagamos para vocês construírem? ＋

 You do. Our standard contract transfers full ownership of the project-specific code, model weights, training data (where the data is yours) and documentation to you on final acceptance. We don't keep a back-door licence and we don't lock you into a maintenance contract.

What we retain is our pre-existing toolbox — reusable libraries, calibration utilities, generic data parsers, the OWL EYE® core — which we license to you, royalty-free, for use on the delivered tool. This is the same boundary that any reputable engineering firm draws: we bring the toolbox, you keep the deliverable.

If you want a different IP arrangement — for example a joint patent on a novel algorithm, or a co-developed model that we both reuse with consent — we structure that explicitly in the project contract. Tell us up-front in the Discovery workshop.

Full engagement details on our Industrial Perception AI page.
Ihnen. Unser Standard-Vertrag überträgt das volle Eigentum am projektspezifischen Code, an den Modell-Gewichten, an Trainingsdaten (soweit die Daten Ihnen gehören) und an der Dokumentation bei der Abnahme an Sie. Wir behalten keine Hintertür-Lizenz und binden Sie nicht an einen Wartungsvertrag.

Was bei uns bleibt, ist unser vorhandener Werkzeugkasten — wiederverwendbare Bibliotheken, Kalibrier-Utilities, generische Daten-Parser, der OWL EYE®-Kern — den wir Ihnen lizenzgebührenfrei für das gelieferte Tool lizenzieren. Das ist dieselbe Grenze, die jedes seriöse Ingenieurbüro zieht: Wir bringen den Werkzeugkasten, Sie behalten das Ergebnis.

Wenn Sie eine andere IP-Vereinbarung wollen — etwa ein gemeinsames Patent auf einen neuartigen Algorithmus oder ein gemeinsam entwickeltes Modell, das wir beide mit Einwilligung wiederverwenden — regeln wir das explizit im Projektvertrag. Sagen Sie es vorab im Discovery-Workshop.

Vollständige Details auf unserer Seite zur Industriellen Objekterkennung.
You do. Our standard contract transfers full ownership of the project-specific code, model weights, training data (where the data is yours) and documentation to you on final acceptance. We don't keep a back-door licence and we don't lock you into a maintenance contract.

What we retain is our pre-existing toolbox — reusable libraries, calibration utilities, generic data parsers, the OWL EYE® core — which we license to you, royalty-free, for use on the delivered tool. This is the same boundary that any reputable engineering firm draws: we bring the toolbox, you keep the deliverable.

If you want a different IP arrangement — for example a joint patent on a novel algorithm, or a co-developed model that we both reuse with consent — we structure that explicitly in the project contract. Tell us up-front in the Discovery workshop.

Full engagement details on our Industrial Perception AI page.
You do. Our standard contract transfers full ownership of the project-specific code, model weights, training data (where the data is yours) and documentation to you on final acceptance. We don't keep a back-door licence and we don't lock you into a maintenance contract.

What we retain is our pre-existing toolbox — reusable libraries, calibration utilities, generic data parsers, the OWL EYE® core — which we license to you, royalty-free, for use on the delivered tool. This is the same boundary that any reputable engineering firm draws: we bring the toolbox, you keep the deliverable.

If you want a different IP arrangement — for example a joint patent on a novel algorithm, or a co-developed model that we both reuse with consent — we structure that explicitly in the project contract. Tell us up-front in the Discovery workshop.

Full engagement details on our Industrial Perception AI page.
You do. Our standard contract transfers full ownership of the project-specific code, model weights, training data (where the data is yours) and documentation to you on final acceptance. We don't keep a back-door licence and we don't lock you into a maintenance contract.

What we retain is our pre-existing toolbox — reusable libraries, calibration utilities, generic data parsers, the OWL EYE® core — which we license to you, royalty-free, for use on the delivered tool. This is the same boundary that any reputable engineering firm draws: we bring the toolbox, you keep the deliverable.

If you want a different IP arrangement — for example a joint patent on a novel algorithm, or a co-developed model that we both reuse with consent — we structure that explicitly in the project contract. Tell us up-front in the Discovery workshop.

Full engagement details on our Industrial Perception AI page.
You do. Our standard contract transfers full ownership of the project-specific code, model weights, training data (where the data is yours) and documentation to you on final acceptance. We don't keep a back-door licence and we don't lock you into a maintenance contract.

What we retain is our pre-existing toolbox — reusable libraries, calibration utilities, generic data parsers, the OWL EYE® core — which we license to you, royalty-free, for use on the delivered tool. This is the same boundary that any reputable engineering firm draws: we bring the toolbox, you keep the deliverable.

If you want a different IP arrangement — for example a joint patent on a novel algorithm, or a co-developed model that we both reuse with consent — we structure that explicitly in the project contract. Tell us up-front in the Discovery workshop.

Full engagement details on our Industrial Perception AI page.
Você é. Nosso contrato padrão transfere a propriedade total do código específico do projeto, dos pesos do modelo, dos dados de treinamento (quando os dados são seus) e da documentação para você na aceitação final. Não mantemos uma licença "porta dos fundos" e não prendemos você em um contrato de manutenção.

O que retemos é a nossa toolbox pré-existente — bibliotecas reutilizáveis, utilitários de calibração, parsers genéricos de dados, o núcleo do OWL EYE® — que licenciamos para você, isenta de royalties, para uso na ferramenta entregue. Esse é o mesmo limite que qualquer empresa de engenharia respeitável estabelece: nós trazemos a caixa de ferramentas, você fica com o entregável.

Se você quiser um arranjo de PI diferente — por exemplo, uma patente conjunta sobre um algoritmo inovador ou um modelo co-desenvolvido que ambos reutilizamos com consentimento — estruturamos isso explicitamente no contrato do projeto. Nos avise antecipadamente no workshop Discovery.

Detalhes completos do engajamento em nossa página Industrial Perception AI.

Can you use our existing cameras and LiDAR sensors?Können Sie unsere vorhandenen Kameras und LiDAR-Sensoren weiterverwenden?Can you use our existing cameras and LiDAR sensors?Can you use our existing cameras and LiDAR sensors?Can you use our existing cameras and LiDAR sensors?Can you use our existing cameras and LiDAR sensors?Vocês conseguem usar as nossas câmeras e sensores LiDAR existentes? ＋

 Yes — in most cases. Our toolchain is sensor-agnostic on the data layer. If your sensors deliver a standard format (PCD, LAS, E57 for point clouds; RTSP, GigE Vision, USB3 Vision for cameras) we can ingest them in a day. We've worked with Riegl, Livox, Faro, Hesai, Velodyne, Ouster, Basler, FLIR, Allied Vision and IDS cameras.

We'll tell you honestly when the existing hardware isn't the right tool. Common cases where we recommend a replacement:

- The existing LiDAR is too narrow (FoV or range) to cover the area you want classified — usually solved by repositioning before buying anything new.
- The camera is auto-exposing on a high-contrast scene (sun + shadow at a gate), which breaks classification reliability — solved by switching to a manual-exposure industrial camera with HDR sensor.
- The existing sensor's vendor lock-in (proprietary stream format, no documented SDK) makes integration cost more than a Livox replacement.

We don't earn a margin on reselling hardware to you. If you do want us to source new sensors, we do that through our normal Hardware & products channel at list price. See Industrial Perception AI for how we scope it.
In den meisten Fällen ja. Unsere Toolchain ist auf der Datenebene sensor-agnostisch. Wenn Ihre Sensoren ein Standard-Format liefern (PCD, LAS, E57 für Punktwolken; RTSP, GigE Vision, USB3 Vision für Kameras), binden wir sie an einem Tag an. Wir haben mit Riegl, Livox, Faro, Hesai, Velodyne, Ouster, Basler, FLIR, Allied Vision und IDS gearbeitet.

Wir sagen Ihnen ehrlich, wenn die vorhandene Hardware nicht das richtige Werkzeug ist. Übliche Fälle, in denen wir zum Tausch raten:

- Der vorhandene LiDAR ist zu schmal (FoV oder Reichweite), um den gewünschten Bereich abzudecken — meist durch Repositionierung gelöst, bevor neu gekauft wird.
- Die Kamera auto-belichtet auf einer Szene mit hohem Kontrast (Sonne + Schatten am Tor), das bricht die Klassifikations-Zuverlässigkeit — gelöst durch Wechsel auf eine manuell belichtbare Industrie-Kamera mit HDR-Sensor.
- Vendor-Lock-in des vorhandenen Sensors (proprietärer Stream, kein dokumentiertes SDK) macht die Anbindung teurer als ein Livox-Ersatz.

Wir verdienen nicht am Weiterverkauf von Hardware an Sie. Wenn Sie wollen, dass wir neue Sensoren beschaffen, läuft das über unseren regulären Kanal Handelsware & Produkte zum Listenpreis. Scoping-Modell siehe Industrielle Objekterkennung.
Yes — in most cases. Our toolchain is sensor-agnostic on the data layer. If your sensors deliver a standard format (PCD, LAS, E57 for point clouds; RTSP, GigE Vision, USB3 Vision for cameras) we can ingest them in a day. We've worked with Riegl, Livox, Faro, Hesai, Velodyne, Ouster, Basler, FLIR, Allied Vision and IDS cameras.

We'll tell you honestly when the existing hardware isn't the right tool. Common cases where we recommend a replacement:

- The existing LiDAR is too narrow (FoV or range) to cover the area you want classified — usually solved by repositioning before buying anything new.
- The camera is auto-exposing on a high-contrast scene (sun + shadow at a gate), which breaks classification reliability — solved by switching to a manual-exposure industrial camera with HDR sensor.
- The existing sensor's vendor lock-in (proprietary stream format, no documented SDK) makes integration cost more than a Livox replacement.

We don't earn a margin on reselling hardware to you. If you do want us to source new sensors, we do that through our normal Hardware & products channel at list price. See Industrial Perception AI for how we scope it.
Yes — in most cases. Our toolchain is sensor-agnostic on the data layer. If your sensors deliver a standard format (PCD, LAS, E57 for point clouds; RTSP, GigE Vision, USB3 Vision for cameras) we can ingest them in a day. We've worked with Riegl, Livox, Faro, Hesai, Velodyne, Ouster, Basler, FLIR, Allied Vision and IDS cameras.

We'll tell you honestly when the existing hardware isn't the right tool. Common cases where we recommend a replacement:

- The existing LiDAR is too narrow (FoV or range) to cover the area you want classified — usually solved by repositioning before buying anything new.
- The camera is auto-exposing on a high-contrast scene (sun + shadow at a gate), which breaks classification reliability — solved by switching to a manual-exposure industrial camera with HDR sensor.
- The existing sensor's vendor lock-in (proprietary stream format, no documented SDK) makes integration cost more than a Livox replacement.

We don't earn a margin on reselling hardware to you. If you do want us to source new sensors, we do that through our normal Hardware & products channel at list price. See Industrial Perception AI for how we scope it.
Yes — in most cases. Our toolchain is sensor-agnostic on the data layer. If your sensors deliver a standard format (PCD, LAS, E57 for point clouds; RTSP, GigE Vision, USB3 Vision for cameras) we can ingest them in a day. We've worked with Riegl, Livox, Faro, Hesai, Velodyne, Ouster, Basler, FLIR, Allied Vision and IDS cameras.

We'll tell you honestly when the existing hardware isn't the right tool. Common cases where we recommend a replacement:

- The existing LiDAR is too narrow (FoV or range) to cover the area you want classified — usually solved by repositioning before buying anything new.
- The camera is auto-exposing on a high-contrast scene (sun + shadow at a gate), which breaks classification reliability — solved by switching to a manual-exposure industrial camera with HDR sensor.
- The existing sensor's vendor lock-in (proprietary stream format, no documented SDK) makes integration cost more than a Livox replacement.

We don't earn a margin on reselling hardware to you. If you do want us to source new sensors, we do that through our normal Hardware & products channel at list price. See Industrial Perception AI for how we scope it.
Yes — in most cases. Our toolchain is sensor-agnostic on the data layer. If your sensors deliver a standard format (PCD, LAS, E57 for point clouds; RTSP, GigE Vision, USB3 Vision for cameras) we can ingest them in a day. We've worked with Riegl, Livox, Faro, Hesai, Velodyne, Ouster, Basler, FLIR, Allied Vision and IDS cameras.

We'll tell you honestly when the existing hardware isn't the right tool. Common cases where we recommend a replacement:

- The existing LiDAR is too narrow (FoV or range) to cover the area you want classified — usually solved by repositioning before buying anything new.
- The camera is auto-exposing on a high-contrast scene (sun + shadow at a gate), which breaks classification reliability — solved by switching to a manual-exposure industrial camera with HDR sensor.
- The existing sensor's vendor lock-in (proprietary stream format, no documented SDK) makes integration cost more than a Livox replacement.

We don't earn a margin on reselling hardware to you. If you do want us to source new sensors, we do that through our normal Hardware & products channel at list price. See Industrial Perception AI for how we scope it.
Sim — na maioria dos casos. Nossa toolchain é agnóstica a sensor na camada de dados. Se os seus sensores entregam um formato padrão (PCD, LAS, E57 para nuvens de pontos; RTSP, GigE Vision, USB3 Vision para câmeras), conseguimos ingeri-los em um dia. Já trabalhamos com câmeras Riegl, Livox, Faro, Hesai, Velodyne, Ouster, Basler, FLIR, Allied Vision e IDS.

Vamos dizer honestamente quando o hardware existente não for a ferramenta certa. Casos comuns em que recomendamos substituição:

- O LiDAR existente é muito estreito (FoV ou alcance) para cobrir a área que você quer classificar — geralmente resolvido reposicionando antes de comprar qualquer coisa nova.
- A câmera está fazendo auto-exposição em uma cena de alto contraste (sol + sombra em um portão), o que quebra a confiabilidade da classificação — resolvido trocando para uma câmera industrial de exposição manual com sensor HDR.
- O bloqueio de fornecedor do sensor existente (formato de stream proprietário, sem SDK documentado) faz com que a integração custe mais do que uma substituição por Livox.

Não ganhamos margem revendendo hardware para você. Se você quiser que a gente forneça sensores novos, fazemos isso através do nosso canal normal de Hardware & products a preço de tabela. Consulte Industrial Perception AI para saber como dimensionamos.
 