A GNSS/LiDAR/IMU Pose Estimation System Based on Collaborative Fusion of Factor Map and Filtering

One of the core issues of mobile measurement is the pose estimation of the carrier. The classic Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) integrated navigation scheme has high positioning accuracy but is vulnerable to Global Navigation Satellite System (GNSS) signal occlusion and multipath effect. Simultaneous Localization and Mapping (SLAM) is not affected by signal occlusion, but there are problems such as error accumulation and scene degradation. The multi-sensor fusion scheme combining the two technologies can effectively expand the scene coverage and improve the positioning accuracy and system robustness. However, the current scheme has some limitations. On the one hand, GNSS plays a less important role in most SLAM systems, only for initialization or as a closed-loop factor and other auxiliary work. On the other hand, in the fusion method, most of the current systems only use filtering or graph optimization, without taking into account the advantages of both. Aiming at pose estimation for mobile carriers, this paper combines the advantages of the global optimization of the factor graph and the local optimization of filtering and proposes a GNSS-IMU-LiDAR Constraint Kalman Filter (abbreviated as GIL-CKF), which has the characteristics of full coverage and effectively improving absolute accuracy and high output frequency. The scheme proposed in this paper consists of three parts. Firstly, an extensible factor map is used to fuse the positioning information from different sources, including GNSS, IMU, LiDAR, and a closed-loop map, to maintain a high-precision SLAM system, and the output is used as Multi-Sensor-Fusion-Odometry (MSFO). Then, the scene is divided according to the GNSS quality factor, and a Scene Optimizer (SO) is designed to filter GNSS pose and MSFO. Finally, the results are input into the Extended Kalman Filter (EKF) together with the original IMU data for further optimization and smoothing. The experimental results show that the integration of high-precision GNSS positioning information with IMU, LiDAR, a closed-loop map, and other information through the factor map can effectively suppress error accumulation and improve the overall accuracy of the SLAM system. The SO based on GNSS indicators can fully retain high-precision GNSS positioning information, effectively play their respective advantages of filtering and graph optimization, and alleviate the conflict between global and local optimization. SO with EKF filtering furthers optimization, can improve the output frequency, and smooth the trajectory. GIL-CKF can effectively improve the accuracy and robustness of pose estimation and has obvious advantages in multi-sensor scene complementarity, partial road section accuracy improvement, and high input frequency.