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. We release our code and model at https://github.com/LJacksonPan/RaTrack.

翻译：摘要：移动自主系统依赖于对动态环境的精确感知。在三维世界中稳健追踪移动物体，对于轨迹预测、避障与路径规划等应用至关重要。尽管当前多数方法利用激光雷达或摄像头实现多目标追踪（MOT），但4D成像雷达的潜力尚未得到充分发掘。针对4D雷达数据中的噪声和点云稀疏性挑战，我们提出RaTrack，一种专为雷达追踪设计的创新方案。该方法绕过传统对特定物体类型与三维边界框的依赖，聚焦于运动分割与聚类，并通过运动估计模块进行增强。在View-of-Delft数据集上的评估表明，RaTrack在移动物体的追踪精度上显著超越现有最优方法。我们已在https://github.com/LJacksonPan/RaTrack 公开代码与模型。