用时序偏移和外观特征提升稀疏雷达点云中的动体追踪精度
Radar Tracker: Moving Instance Tracking in Sparse and Noisy Radar Point Clouds
- 引入时序偏移预测，实现中心点直接关联
- 结合几何与外观特征，提升追踪稳定性
- 在RadarScenes数据集上超越当前最优方法
机器人和自动驾驶车辆需感知周围动态，移动目标的分割与追踪对路径规划和避障至关重要。本文针对雷达感知下的移动实例追踪任务，提出一种基于学习的雷达追踪器。该方法通过引入时序偏移预测，实现直接中心点关联，并利用额外运动线索提升分割性能。采用注意力机制融合外观特征，增强稀疏雷达扫描下的追踪效果。最终关联融合几何与外观特征，克服纯中心点追踪的局限性。在RadarScenes数据集的移动实例追踪基准上，本方法优于当前最优技术。
原文摘要 · Abstract (English)
Robots and autonomous vehicles should be aware of what happens in their surroundings. The segmentation and tracking of moving objects are essential for reliable path planning, including collision avoidance. We investigate this estimation task for vehicles using radar sensing. We address moving instance tracking in sparse radar point clouds to enhance scene interpretation. We propose a learning-based radar tracker incorporating temporal offset predictions to enable direct center-based association and enhance segmentation performance by including additional motion cues. We implement attention-based tracking for sparse radar scans to include appearance features and enhance performance. The final association combines geometric and appearance features to overcome the limitations of center-based tracking to associate instances reliably. Our approach shows an improved performance on the moving instance tracking benchmark of the RadarScenes dataset compared to the current state of the art.
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