Angular Super-Resolution Radar SLAM
Zhiyuan Zeng, Xiangwei Dang, Yanlei Li, Xiangxi Bu, Xingdong Liang
Abstract
Radar SLAM has attracted wide attention due to its all-day and all-weather working characteristics in the last decade. The existing radar SLAM systems mainly adopt mechanically pivoting radar with simple principle and high resolution, but this kind of radar has disadvantages such as low frame rate, distortion of the radar image, and high cost. Although array snapshot radar has the advantages of high frame rate and low cost, its low azimuth resolution, multipath reflection, and angular glint limit its application in SLAM. This paper proposes a SLAM system developed on array snapshot radar. The system realizes angular super-resolution radar imaging through compressed sensing, which effectively solves the problems of poor azimuth resolution and multipath reflection of array snapshot radar. We also propose the corresponding point cloud extraction method and scan matching method, this method performs a centroid iterative closest point algorithm between the submaps, thereby effectively improving the interference of noise and angular glint. Experimental results show that our proposed array snapshot radar SLAM system can reduce the mean absolute trajectory error by more than 3 times compared with the existing system, and can show accuracy and robustness in various environments.