TC$^{2}$LI-SLAM: A Tightly-Coupled Camera-LiDAR-Inertial SLAM System

In this letter, we propose TC
$^{2}$
LI-SLAM, a novel and tightly-coupled camera-LiDAR-inertial SLAM system with a separated front end and a unified back end. The front end accepts non-identical frequency inputs and performs real-time pose estimation while incorporating different inputs into keyframes for the back end, benefiting from our novel keyframe-oriented data synchronization method. To improve the accuracy of the back-end optimization, we innovatively leverage multi-sensor consecutive co-vision constraints in a keyframe-based local bundle adjustment problem. To bridge the gap between different residual terms from different sensors in the optimization problem, we propose a cross-modal residual standardization method that can be easily applied. Furthermore, the proposed system can degrade into TC
$^{2}$
L-SLAM, a tightly-coupled camera-LiDAR SLAM system without IMU, to cope with situations where inertial measurements are not available or reliable. Finally, both TC
$^{2}$
LI-SLAM and TC
$^{2}$
L-SLAM are tested on public and self-recorded datasets. The results show their superior performance in terms of accuracy compared with the state-of-the-art SLAM systems, with at most 33% accuracy improvement over ORB-SLAM3 on the KITTI dataset.