Fast 3D Point Cloud Target Tracking based on Polar-Voxel Encoding

The century-old development of the automotive industry has spawned one of the greatest Cyber-Physical Systems (CPSs) in the future-unmanned vehicles. The vehicle can obtain environmental information through different sensors, map it to the virtual coordinate system of the vehicle body to make decisions, and finally generate control instructions. However, a series of factors, such as complex road scenes, defective and irregular target sparse sampling, and large coding space, pose challenges to accurate, efficient, and stable perception results. To overcome the most challenging problem of dynamic target tracking, this paper designs a two-stage detection model based on non-uniform polar voxelization sampling of irregular 3D point cloud, which is used with local registration-based search to achieve efficient multi-target tracking. Non-uniform voxelization not only balances the spatial sampling and encoding efficiency of the point cloud for the backbone, but also adapts to the feature aggregation of the detection head, thereby achieving double acceleration. Finally, we tested our model on KITTI Tracking data. The comparison results show that the calculation speed of the final model is greatly improved and the tracking accuracy is competitive in all categories.