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The multi-modal multi-object tracking pipeline fuses 3D LiDAR detections and Camera-LiDAR detections to form the heart of the multi-modal perception pipeline. It also provides a feedback mechanism between it and the multi-object tracking module to enable smooth, continuous trajectory tracking for dynamic objects over time.
Checklist
Write a launch file that successfully launches:
Occupancy grid map pipeline.
LiDAR pipeline.
Camera-LiDAR pipeline.
Object association merger module.
Interpolator module.
Multi-object tracking module.
Acceptance Criteria
As confirmed by testing:
The ROI cluster fusion module from the camera-LiDAR pipeline outputs its labeled cluster point cloud as DetectedObjectsWithFeatures to the Interpolator module, which transforms them into DetectedObjects.
Object association merger module takes in DetectedObjects from both the 3D LiDAR object detection pipeline (from the occupancy grid map-based validator module), as well as the DetectedObjects from the interpolator module.
The object association merger module outputs DetectedObjects to the multi-object tracker module, which publishes TrackedObjects back to the interpolator module in a feedback loop for continuous detection and tracking.
Priority
High (3/3)
The text was updated successfully, but these errors were encountered:
Description
The multi-modal multi-object tracking pipeline fuses 3D LiDAR detections and Camera-LiDAR detections to form the heart of the multi-modal perception pipeline. It also provides a feedback mechanism between it and the multi-object tracking module to enable smooth, continuous trajectory tracking for dynamic objects over time.
Checklist
Write a launch file that successfully launches:
Acceptance Criteria
As confirmed by testing:
Priority
High (3/3)
The text was updated successfully, but these errors were encountered: