Radar Tracker: Moving Instance Tracking in Sparse and Noisy Radar Point Clouds
This addresses the problem of reliable scene interpretation for autonomous vehicles using radar sensing, representing an incremental improvement in a domain-specific application.
The paper tackles moving instance tracking in sparse and noisy radar point clouds for autonomous vehicles, proposing a learning-based radar tracker with temporal offset predictions and attention-based features, which shows improved performance on the RadarScenes benchmark compared to the state of the art.
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.