GLO-SLAM: a slam system optimally combining GPS and LiDAR odometry

Abstract

Purpose Large-scale and precise three-dimensional (3D) map play an important role in autonomous driving and robot positioning. However, it is difficult to get accurate poses for mapping. On one hand, the global positioning system (GPS) data are not always reliable owing to multipath effect and poor satellite visibility in many urban environments. In another hand, the LiDAR-based odometry has accumulative errors. This paper aims to propose a novel simultaneous localization and mapping (SLAM) system to obtain large-scale and precise 3D map. Design/methodology/approach The proposed SLAM system optimally integrates the GPS data and a LiDAR odometry. In this system, two core algorithms are developed. To effectively verify reliability of the GPS data, VGL (the abbreviation of Verify GPS data with LiDAR data) algorithm is proposed and the points from LiDAR are used by the algorithm. To obtain accurate poses in GPS-denied areas, this paper proposes EG-LOAM algorithm, a LiDAR odometry with local optimization strategy to eliminate the accumulative errors by means of reliable GPS data. Findings On the KITTI data set and the customized outdoor data set, the system is able to generate high-precision 3D map in both GPS-denied areas and areas covered by GPS. Meanwhile, the VGL algorithm is proved to be able to verify reliability of the GPS data with confidence and the EG-LOAM outperform the state-of-the-art baselines. Originality/value A novel SLAM system is proposed to obtain large-scale and precise 3D map. To improve the robustness of the system, the VGL algorithm and the EG-LOAM are designed. The whole system as well as the two algorithms have a satisfactory performance in experiments.

LiDAR-based SLAM for robotic mapping: state of the art and new frontiers

“…Multi-source fusion SLAM systems based on 3D LiDAR are another research hotspot. Considering that single sensors such as LiDAR, camera and IMU are inaccurate and fragile, researchers have increasingly developed multi-source fusion SLAM solutions (Shan et al , 2021; Tang et al , 2023; Lin et al , 2021; Zheng et al , 2022). Deep learning-augmented LiDAR-based semantic SLAM.…”

LiDAR-Based Sensor Fusion SLAM and Localization for Autonomous Driving Vehicles in Complex Scenarios

“…To improve the robustness of pose-graph SLAM, study [ 29 ] proposed a novel formulation that allows the back-end to change parts of the topological structure of the graph during the optimization process and progress experiments by loop closure constraints. To obtain accurate positions for mapping in large-scale environment, study [ 30 ] proposed global positioning system (GPS) and LiDAR odometry (GLO)-SLAM, which uses LiDAR to verify the reliability of GNSS, and the LiDAR odometry also will be optimized by means of reliable GPS data. In addition, study [ 31 ] added IMU/odometry pre-integration results under the framework of graph optimization, which effectively reduced navigation drift.…”

Guest editorial

“…To enable a robot to accurately build a 3D map in industrial environments, even when GPS signals are invalid, Lin et al propose a robust and precise SLAM system which optimally integrates the GPS data and a Light Detection and Ranging (LiDAR) odometry (Lin et al, 2021). To effectively verify reliability of the GPS data, Verifying GPS data with Lidar data (VGL) algorithm is proposed and the algorithm uses the points from LiDAR.…”