基于激光雷达的无人驾驶3D多目标跟踪

LiDAR-based 3D Multi-object Tracking for Unmanned Vehicles

无人驾驶汽车行驶是连续时空的三维运动,汽车周围的目标不可能突然消失或者出现,因此,对于感知层而言,稳定可靠的多目标跟踪(Multi-object tracking,MOT)意义重大.针对传统的目标关联和固定生存周期(Birth and death memory,BDM)管理的不足,提出基于边界交并比(Border intersection over union,BIoU)度量的目标关联和自适应生存周期管理策略.BIoU综合了欧氏距离和交并比(Intersection over union,IoU)的优点,提高了目标关联的精度.自适应生存周期管理将目标轨迹置信度与生存周期相联系,显著减少了目标丢失和误检.在KITTI多目标跟踪数据集上的实验验证了该方法的有效性.

Unmanned vehicle is a three-dimensional motion in continuous time and space,and the object around the vehicle can not disappear or appear suddenly.Therefore,for the perception system,stable and robust multi-object tracking(MOT)is of great significance.Aiming at the shortcomings of object association and fixed birth and death memory(BDM)in the traditional one,the border intersection over union(BIoU)based object association and adaptive life cycle management strategy are put forward.The BIoU takes into account the advantages of both Euclidean distance and intersection over union(IoU)to improve the accuracy of object association.The adaptive life cycle management associates the object trajectory confidence with the life cycle,which significantly reduces object missing and false detection.The effectiveness of the proposed approach is verified through experiments on the KITTI multi-object tracking dataset.