A tightly coupled SLAM method for precise urban mapping

Aiming to reduce the cumulative error and improve the robustness of SLAM system in accurate urban mapping, a tightly coupled laser SLAM algorithm that combined LiDAR, inertial measurement unit (IMU), and global navigation satellite system (GNSS) was developed. The proposed method achieved high accuracy point cloud registration by adding pole-like and plane features that reduced cumulative errors in SLAM. In addition, a GNSS corner-based constraint was used to improve the accuracy of the global map construction. This study compared the proposed method with three mainstream SLAM methods (i.e., LOAM, LeGO-LOAM, and LIO-SAM) in four common urban scenes (i.e., open park, underground garage, urban park, and road). The test results showed that LOAM and LeGO-LOAM have poor stabilities in complex urban scenes. The LIO-SAM and proposed method have successfully realized the mapping of all scenes. Compared to LIO-SAM, the absolute position error (APE) of the proposed method has improved by 32.25% without the GNSS position factor and has improved by 92.03% with the GNSS position factor (APE < 10 cm). Moreover, the absolute coordinate error of generated point cloud by the proposed method in the open park scene was less than 5 cm, which demonstrates the proposed method can fulfill the requirements of centimeter-level urban mapping.