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.

移动自主性依赖于对动态环境的精确感知。因此，鲁棒地跟踪三维世界中的移动物体对于轨迹预测、避障和路径规划等应用至关重要。尽管大多数当前方法利用激光雷达或摄像头进行多目标跟踪 (MOT)，但四维成像雷达的能力仍然很少被探索。针对四维雷达数据中的雷达噪声和点稀疏性带来的挑战，我们介绍了一种针对基于雷达的跟踪的创新解决方案 RaTrack。我们的方法摒弃了对特定物体类型和三维边界框的依赖，而是专注于运动分割和聚类，并且通过运动估计模块进一步丰富。在 View-of-Delft 数据集上进行评估，RaTrack 展示了对移动物体的优越跟踪精度，明显超过了最新研究的性能。