視覚慣性教師あり学習によるミリ波レーダー点群の高密度化
Enhancing mmWave Radar Point Cloud via Visual-inertial Supervision
低コストなカメラとIMUの視覚慣性情報を教師信号として使い、ミリ波レーダーの疎な点群を高密度化しつつマルチパス由来の偽点を除去する学習手法mmEMPを提案した。
著者: Cong Fan, Shengkai Zhang, Kezhong Liu, Shuai Wang, Zheng Yang, Wei Wang
分類: cs.RO
原文アブストラクト
Complementary to prevalent LiDAR and camera systems, millimeter-wave (mmWave) radar is robust to adverse weather conditions like fog, rainstorms, and blizzards but offers sparse point clouds. Current techniques enhance the point cloud by the supervision of LiDAR's data. However, high-performance LiDAR is notably expensive and is not commonly available on vehicles. This paper presents mmEMP, a supervised learning approach that enhances radar point clouds using a low-cost camera and an inertial measurement unit (IMU), enabling crowdsourcing training data from commercial vehicles. Bringing the visual-inertial (VI) supervision is challenging due to the spatial agnostic of dynamic objects. Moreover, spurious radar points from the curse of RF multipath make robots misunderstand the scene. mmEMP first devises a dynamic 3D reconstruction algorithm that restores the 3D positions of dynamic features. Then, we design a neural network that densifies radar data and eliminates spurious radar points. We build a new dataset in the real world. Extensive experiments show that mmEMP achieves competitive performance compared with the SOTA approach training by LiDAR's data. In addition, we use the enhanced point cloud to perform object detection, localization, and mapping to demonstrate mmEMP's effectiveness.
