基于激光雷达的3D实时车辆跟踪

3D Real-Time Vehicle Tracking Based on Lidar

3D多目标跟踪算法是智能车辆感知算法的重要组成部分,现有跟踪算法多与检测算法耦合以提高精度,导致算法实时性不足。针对此问题,本文中提出一种基于激光雷达的3D实时车辆跟踪算法。首先,对于激光雷达检测结果杂波较少的工况,提出结构精简的双波门GNN关联算法,有效提升其关联速度及精度;其次,优化关联向量与关联距离,既保证了算法的普适性,又提升其跟踪精度;最后,针对3D目标运动情况使用3D IMM-KF算法解决了3D机动目标的跟踪问题。基于公开数据集KITTI,本文算法在获得266.1 FPS跟踪速度的前提下可实现81.55%的MOTA精度;基于自研无人车平台进行面对遮挡工况的验证,结果表明本算法具有良好的目标跟踪及关联性能。

The 3D multi-object tracking algorithm is an essential part of the intelligent vehicle perception algorithm.The existing tracking algorithm is mostly coupled with the detection algorithm to improve the accuracy,resulting in insufficient real-time performance.To solve this problem,a 3D real-time vehicle tracking algorithm based on lidar is proposed.Firstly,for the working conditions with less clutter in the detection results of lidar,a double-validation gate GNN algorithm with a simple structure is proposed to effectively improve its correlation speed and accuracy;secondly,the correlation vector and correlation distance are optimized,which improves the tracking accuracy while ensuring the generality of the algorithm.Finally,the 3D IMM-KF algorithm is used to solve the tracking problem of 3D object with changing dynamics.The proposed algorithm achieves a MOTA accuracy of 81.55%at a tracking speed of 266.1 FPS according to the public data set KITTI.Based on the self-developed unmanned vehicle platform,the verification of facing occlusion conditions shows that the algorithm has good object tracking and correlation performance.