Tightly-coupled Lidar-Visual-Inertial SLAM for Mobile Robot

Simultaneous Localization and Mapping (SLAM) enables robots to perform localization and mapping in unknown environments. Currently, mainstream single-sensor Lidar SLAM (Visual SLAM) tends to diverge and fail when faced with unstructured or textureless scenes. To address this issue, this paper proposes a robust tightly-coupled Lidar-Vision-Inertial Odometry framework (LRI-LVIO) that achieves high-precision and robust SLAM. LRI-LVIO consists of two subsystems: a visual-inertial system and a lidar-inertial system. These two subsystems are tightly coupled through an error-state iterative Kalman filter, allowing one subsystem to maintain stable operation even if the other fails, thus enhancing the robustness of SLAM in textureless and featureless environments. Additionally, the depth of visual feature points is recovered using lidar point cloud information, improving the algorithm's efficiency and accuracy. Finally, LRI-LVIO incorporates loop closure detection based on lidar point cloud keyframes, further reducing cumulative errors and enhancing localization precision. Experiments conducted in various indoor and outdoor environments show that by combining the advantages of each sensor, LRI-LVIO achieves higher accuracy and stronger robustness compared to single-sensor SLAM.