A High-Precision LiDAR-Inertial Odometry via Kalman Filter and Factor Graph Optimization

For simultaneous localization and mapping (SLAM) in large-scale scenarios, the influence of long-distance and high-speed motion cannot be ignored because the risk of huge odometry drift will increase. To solve this problem, we propose the LiDAR -inertial odometry (LIO) method via Kalman filter and factor graph optimization (LIO-FILO), which provides real-time, high-frequency, and high-precision odometry. The LIO system consisting of three modules is established to fit various application scenarios. The state estimation module provides the pose states to be optimized and receives timely feedback from the pose optimization module. In the loop closure module, LIO-FILO constructs a multilayer structure loop closure detection method, including different schemes of detection, and the loop closure factor is constructed by the pose transformation matrix calculated by ICP. In the pose optimization module, the fast-build adjacent constraint factors and the loop closure factors are added to the factor graph to get the optimized result based on the GTSAM library. The real-world experiments show that LIO-FILO can mitigate the odometry drift to achieve accurate mapping results and obtain higher precision odometry compared with the existing advanced SLAM methods.