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：移动自主性依赖于对动态环境的精确感知。因此，在三维世界中稳健跟踪运动目标对于轨迹预测、避障和路径规划等应用至关重要。尽管大多数现有方法利用激光雷达或相机进行多目标跟踪，但4D成像雷达的能力在很大程度上尚未被探索。考虑到雷达噪声和4D雷达数据中点云稀疏性带来的挑战，我们提出了RaTrack——一种专为基于雷达的跟踪设计的创新解决方案。我们的方法摒弃了对特定目标类型和三维边界框的典型依赖，专注于运动分割和聚类，并通过运动估计模块加以增强。在View-of-Delft数据集上的评估表明，RaTrack在运动目标跟踪精度上表现出色，大幅超越了现有技术水平。