4D iRIOM: 4D Imaging Radar Inertial Odometry and Mapping

Millimeter wave radar can measure distances, directions, and Doppler velocity for objects in harsh conditions such as fog. The 4D imaging radar with both vertical and horizontal data resembling an image can also measure objects' height. Previous studies have used 3D radars for ego-motion estimation. But few methods leveraged the rich data of imaging radars, and they usually omitted the mapping aspect, thus leading to inferior odometry accuracy. This letter presents a real-time imaging radar inertial odometry and mapping method, iRIOM, based on the submap concept. To deal with moving objects and multipath reflections, we use the graduated non-convexity method to robustly and efficiently estimate ego-velocity from a single scan. To measure the agreement between sparse non-repetitive radar scan points and submap points, the distribution-to-multi-distribution distance for matches is adopted. The ego-velocity, scan-to-submap matches are fused with the 6D inertial data by an iterative extended Kalman filter to get the platform's 3D position and orientation. A loop closure module is also developed to curb the odometry module's drift. To our knowledge, iRIOM based on the two modules is the first 4D radar inertial SLAM system. On our and third-party data, we show iRIOM's favorable odometry accuracy and mapping consistency against the FastLIO-SLAM and the EKFRIO. Also, the ablation study reveal the benefit of inertial data versus the constant velocity model, and scan-to-submap matching versus scan-to-scan matching.

Automated summary

This paper presents a real-time imaging radar inertial odometry and mapping method based on the submap concept. It utilizes millimeter wave radar to measure distances, directions, and Doppler velocity, as well as objects' height. The method leverages the rich data of imaging radars and robustly estimates ego-velocity from a single scan using the graduated non-convexity method. The scan-to-submap matches, along with 6D inertial data, are fused to obtain the platform's 3D position and orientation using an iterative extended Kalman filter. A loop closure module is developed to mitigate odometry drift.