Bayesian network to track objects using scan points using multiple LiDAR sensors

摘要

A system and method for fusing the outputs from multiple LiDAR sensors on a vehicle. The method includes providing object files for objects detected by the sensors at a previous sample time, where the object files identify the position, orientation and velocity of the detected objects. The method also includes receiving a plurality of scan returns from objects detected in the field-of-view of the sensors at a current sample time and constructing a point cloud from the scan returns. The method then segments the scan points in the point cloud into predicted clusters, where each cluster initially identifies an object detected by the sensors. The method matches the predicted clusters with predicted object models generated from objects being tracked during the previous sample time. The method creates new object models, deletes dying object models and updates the object files based on the object models for the current sample time.

主权项

1. A method for fusing outputs from multiple LiDAR sensors on a vehicle, said method comprising:
providing object files for objects detected by the LiDAR sensors at a previous sample time, said object files having object models that identify a position, orientation and velocity of the objects detected by the sensors; tracking the object models of the objects detected by the sensors; projecting tracked object models in the object files from the previous scan time to provide predicted object models; receiving a plurality of scan returns from objects detected in a field-of-view of the sensors at a current sample time; constructing a scan point cloud from the scan returns; segmenting the scan points in the point cloud into predicted scan clusters where each scan cluster initially identifies an object detected by the sensors; matching the predicted scan clusters with the predicted object models; merging predicted object models that have been identified as separate scan clusters in the previous sample time but are now identified as a single scan cluster in the current sample time; splitting predicted object models that have been identified as a single scan cluster in the previous sample time but are now identified as separate scan clusters in the current sample time; creating new object models for detected objects in the current sample time that were not present in the previous sample time; deleting object models that are no longer present in the predicted scan clusters; providing object model updates based on the merged object models, split object models, new object models and deleted object models; and updating the object files for the current sample time with the object model updates.