One Robust Loosely Coupled 4D Millimeter-Wave Image Radar SLAM
Method 2023-01-7051
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.