One Robust Loosely Coupled 4D Millimeter-Wave Image Radar SLAM Method

Abstract

<div class="section abstract"><div class="htmlview paragraph">In this paper, we introduce one imu radar loosely coupled SLAM method based on our 4D millimeter-wave image radar which it outputs pointcloud containing xyz position information and power information in our autonomous vehicles. at common pointcloud-based slam such as lidar slam usually adopt imu-lidar tightly coupled structure, which slam front end outputs odometry reversly affect imu preintegration. slam system badness occurs when front end odometry drift bigger and bigger or one frame pointcloud match failed. so in our method, we decouple imu and radar odometry crossed relationship, fusing imu and wheel odometry to generate one rough pose trajectory as initial guess value for front end registration, not directly from radar estimated odometry pose, that is to say, front end registration is independent of imu preintegration. besides, we empirically propose one idea juding front end registration result to identify match-less environment and adopt relative wheel odometry pose instead of registration pose when match belief value(mbv) is false. this can handle some degrade environment, such as two-side similar greenbelt. finally, to increase loop detection robustness, we propose two-stage loop detection verify method. first stage is RS(radius search) method, if it passes loop verify, not enter second stage, otherwise enter SC(scan context) second stage, after two stage loop, most real loop can be detected by our slam system. based on above ideas, at multi scene’s datasets, office park, residential area, open road, underground parkingplace etc, we can run our slam system successfully, meanwhile at our office park dataset we compare trajectory precision with tightly-coupled slam structure and the detected loop number with one stage loop method, exprimental result proved our proposed method is valid.</div></div>