Artemis: Contour-Guided 3-D Sensing and Localization With mmWave Radar for Infrastructure-Assisted Autonomous Vehicles

Infrastructure-assisted autonomous driving has become a new paradigm that enables autonomous vehicles to fuse sensor data and improve driving safety, where a key enabling technology for achieving this vision is to real-time and accurate registering 3-D mmWave radar point clouds between the infrastructure and the vehicle. To this end, we propose Artemis, a novel lightweight system capable of achieving real-time registration with decimeter-level localization. Artemis consists of three components: 1) a modal association-based salient object extraction component leverages the complementary advantages of cameras and radars to extract semantics and areas of salient objects for radar point clouds; 2) a salient object shape construction component extracts the shape contour of salient objects based on their inherent geometries; and 3) a contour-guided 3-D point cloud registration component combines two key strategies, keypoint matching strategy and early exit strategy, to quickly select keypoints and transformation directions for achieving accurate registration in real-time. We implement and evaluate Artemis with two multiview datasets collected in the CARLA platform and campus. The experiment results show that Artemis achieves an average registration error of 0.33 m within 32.26 ms.