Trajectory Correction Model for Registering City-scale Mobile Laser Scanning Data

Abstract

Mobile laser scanning (MLS) systems with light detection and ranging (LiDAR) sensors, global navigation satellite system (GNSS) receivers, and inertial measurement unit (IMU) sensors have been widely used in applications such as smart city facility censuses and high-definition mapping. However, because of the complexity of urban environments, there are often decimeter-to-meter-level position deviations between multiple scanning data of the same area. To solve this problem, we propose a trajectory correction model for registering city-scale MLS point cloud data. First, the proposed model segments the trajectory in terms of the data accuracy and then segments the data with segmented subtrajectories while maintaining the relationship of matching pairs in overlapping areas. Second, the proposed model transforms the matching pairs based on multiple metrics registration, which uses the poles and planar feature points extracted from the point cloud data using local geometric features. Finally, the global pose optimization method is used to improve the consistency of the MLS point clouds. In data registration experiments on different urban scenes, the proposed method performed well with high robustness and decreased the position deviations by 70%.