A Robust and Efficient LiDAR-Inertial-Visual Fused Simultaneous Localization and Mapping System with Loop Closure

We have proposed the first-of-its-kind LiDAR-Inertial-Visual-Fused simultaneous localization and mapping (SLAM) system with a strong place recognition capacity. Our proposed SLAM system is consist of visual-inertial odometry (VIO) and LiDAR inertial odometry (LIO) subsystems. We propose the LIO subsystem utilizing the measurement from the LiDAR and the inertial sensors to build the local odometry map, and propose the VIO subsystem which takes in the visual information to construct the 2D-3D associated map. Then, we propose an iterative Kalman Filter-based optimization function to optimize the local project-based 2D-to-3D photo-metric error between the projected image pixels and the local 3D points to make the robust 2D-3D alignment. Finally, we have also proposed the back-end pose graph global optimization and the elaborately designed loop closure detection network to improve the accuracy of the whole SLAM system. Extensive experiments deployed on the UGV in complicated real-world circumstances demonstrate that our proposed LiDAR-Visual-Inertial localization system outperforms the current state-of-the-art in terms of both accuracy and robustness.