Self-built Data AI base
The cornerstone fuel of the robot edge vision brain
Around data collection, annotation flow, QA evaluation and training iteration in real complex scenarios, we continuously provide high-quality data support for the low-speed mobile robot edge vision brain.
Multi-Angle Lighting Simulation
Covers day-night, reflection, low-light and complex indoor/outdoor scenes
Multi-Material Surface Collection
Accumulates real environment samples across different traversal states
Annotation & Training Integration
Supports event filtering, annotation, evaluation and continuous iteration
01 / Data Collection & Training Center
Real-Scene Collection Capabilities
The data collection & training center is built around common deployment environments of low-speed mobile robots, covering multi-angle lighting simulation, different material surfaces, operator SOP boards and multi-camera collection workflows.
By continuously collecting environment, target and task-process samples from real working conditions, it provides a structurally stable, semantically clear and reusable data foundation for edge vision brain training.
Multi-Angle Lighting
Simulates night, side light, strong reflection, overexposure and other complex visual conditions.
Multi-Material Surfaces
Covers wet, rough, reflective and unstructured ground environment samples.
Real Operation Workflows
Preserves real process information of equipment operation, operator actions and task switching.
SOP Standardization
Forms replicable, traceable and comparable data collection standards.
02 / Annotation Platform UI
Unified Annotation & Flow Capabilities
The annotation platform supports unified management of obstacles, traversable areas, semantic attributes and event-level labels, providing a standardized data foundation for model training, evaluation and version release.
From sample filtering, pre-annotation, manual revision and quality validation to training set management, it forms a stable data loop for the edge vision brain.
Object Annotation
Unified management of object categories, area attributes and event information.
Semantic Labels
Structured semantic results for scene understanding tasks.
QA Workflow
Review and re-check mechanisms ensure label consistency and usability.
Training Set Management
Manages traceable training data by task, version and evaluation needs.
03 / Data Loop
A continuous iteration chain from collection to training
Event Collection
Continuously collects hard examples, anomalies and task-critical segments from real scenarios.
Annotation Flow
Forms stable, usable training samples through pre-annotation, manual validation and QA.
Evaluation & Iteration
Combines model evaluation, effect comparison and version regression to continuously optimize edge capabilities.