GV-iRIOM: GNSS-visual-aided 4D radar inertial odometry and mapping in large-scale environments

Abstract

Accurate state estimation is crucial for autonomous navigation in unmanned systems. While traditional visual and lidar systems struggle in adverse conditions such as rain, fog, or smoke, millimeter-wave radar provides robust all-weather localization and mapping capabilities. However, sparse and noisy radar point clouds often compromise localization accuracy and lead to odometry immanent drift. This paper presents GV-iRIOM, a novel millimeter-wave radar localization and mapping system that utilizes a two layer estimation framework, which simultaneously integrates visual, inertial, and GNSS data to improve localization accuracy. The system employs radar inertial odometry and visual inertial odometry as the SLAM front-end. Addressing the varying observation accuracy of 3-axis motion for different azimuth/vertical angles in 4D radar data, we propose an angle-adaptive weighted robust estimation method for radar ego-velocity estimation. Furthermore, we developed a back-end for multi-source information fusion, integrating odometry pose constraints, GNSS observations, and loop closure constraints to ensure globally consistent positioning and mapping. By dynamically initializing GNSS measurements through observability analysis, our system automatically achieves positioning and mapping based on an absolute geographic coordinate framework, and facilitates multi-phase map fusion and multi-robot positioning. Experiments conducted on both in-house data and publicly available datasets validate the system’s robustness and effectiveness. In large-scale scenarios, the absolute localization accuracy is improved by more than 50%, ensuring globally consistent mapping across a variety of challenging environments.