Mobile autonomy relies on the precise perception of dynamic environments. Robustly tracking moving objects in 3D world thus plays a pivotal role for applications like trajectory prediction, obstacle avoidance, and path planning. While most current methods utilize LiDARs or cameras for Multiple Object Tracking (MOT), the capabilities of 4D imaging radars remain largely unexplored. Recognizing the challenges posed by radar noise and point sparsity in 4D radar data, we introduce RaTrack, an innovative solution tailored for radar-based tracking. Bypassing the typical reliance on specific object types and 3D bounding boxes, our method focuses on motion segmentation and clustering, enriched by a motion estimation module. Evaluated on the View-of-Delft dataset, RaTrack showcases superior tracking precision of moving objects, largely surpassing the performance of the state of the art.

翻译：移动自主系统依赖于对动态环境的精确感知。在3D空间中鲁棒地跟踪运动目标对轨迹预测、障碍规避和路径规划等应用具有关键作用。尽管当前大多数方法采用激光雷达或摄像头实现多目标跟踪，但4D成像雷达的潜力仍未被充分挖掘。针对4D雷达数据中雷达噪声与点云稀疏性带来的挑战，我们提出了RaTrack——一种专为雷达跟踪设计的创新方案。该方法摒弃了传统对特定目标类型和3D边界框的依赖，通过运动分割与聚类技术结合运动估计模块实现目标跟踪。在View-of-Delft数据集上的评估表明，RaTrack在运动目标跟踪精度方面表现卓越，大幅超越了现有最优方法的性能。