Fast and accurate registration of large scene vehicle-borne laser point clouds based on road marking information

Abstract

Vehicle-borne Mobile Laser Scanning (MLS) point cloud is one of the key components of 3D spatial information, which provides important geographic data support for the development of smart cities and autonomous driving. However, urban high-rise buildings will obscure the Global Navigation Satellite System (GNSS) positioning signal of the vehicle-borne MLS system, causing the problem of the inconsistent location of revisited point clouds collected in the same area, and the existing methods are still limited by low accuracy and efficiency when registering large-scale point clouds. Aiming at the complexity and timeliness of vehicle-borne point cloud registration, this paper proposes an improved Iterative Closest Point (ICP) point cloud accurate registration method based on road markings. Firstly, the road feature image is generated based on the laser reflection intensity of the ground point cloud, and the Pix2Pix_L1 (P2P_L1) transformation model is introduced to realize the identification and classification of markings. Secondly, we take road classification markings as the registration primitives, a Random Sample Consensus (RANSAC) point cloud coarse registration method based on a Fast Point Feature Histogram (FPFH) descriptor is designed, and the Chi-square distance function is used to improve the stability of corresponding points. Then, according to the normal vector characteristics of marking point clouds, the angle between the normal vectors is constrained to extract edge points of markings and select effective corresponding closest point pairs, and the translation vector is preferentially calculated according to the offset characteristics of point cloud to optimize spatial transformation matrix. Finally, a linear interpolation method is developed to correct the pose of global point clouds. Experimental results show that our method achieves high registration accuracy and computational speed on multiple datasets, and has stronger performance than the most widely used geometric feature registration methods.