Wei Wu

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.

Simultaneous localization and mapping (SLAM) technology based on light detection and ranging (LiDAR) sensors has been widely used in various environmental sensing tasks indoors and outdoors. However, it still lacks effective constraints in structured environments such as corridors and parking lots, and its accuracy needs improvement. Based on this, a planar constraint-assisted LiDAR SLAM algorithm based on the Manhattan World (MW) assumption is proposed in this paper. The algorithm extracts planes from the environment point cloud submap, classifies the planes according to the ground and vertical planes, and calculates the main direction angles of the ground and vertical plane, respectively, to construct constraints. To enhance the stability and robustness of the system, a two-step main direction angle calculation and update strategy are designed, and a hysteresis update is used to avoid the introduction of errors by unoptimized planes. This paper uses a backpack laser scanning system to collect experimental data in various scenes. These data are used to compare our method with three open-source LiDAR SLAM algorithms, that are currently more widely used and perform better. Qualitative and quantitative experiments are conducted to verify the effectiveness of our method. The experimental results show that the absolute accuracy of the point clouds obtained by our method is improved by 77.46% on average compared with the other three algorithms in the environment, conforming to the MW assumption, which verifies the effectiveness of the algorithm.