Composite Ski-Resort Registration Method Based on Laser Point Cloud Information

Abstract

The environment of ski resorts is usually complex and changeable, and there are few characteristic objects in the background, which creates many difficulties for the registration of ski-resort point cloud datasets. However, in the traditional iterative closest point (ICP) algorithm, two points need to have good initial positions, otherwise it is easy to get caught up in local optimizations in registration. Aiming at this problem, according to the topographic features of ski resorts, this paper put forward a ski-resort coarse registration method based on extraction, and matching between feature points is proposed to adjust the initial position of two point clouds. Firstly, the feature points of the common part of the point cloud datasets are extracted based on the SIFT algorithm; secondly, the Euclidean distance between the feature normal vectors is used as the pairing condition to complete the pairing between the feature points in the point cloud datasets; then, the feature point pair is purified by using the included angle of the normal vector; finally, in the process of coarse registration, the rotation matrix and translation vector between point clouds are solved by the unit quaternion method. Experiments demonstrate that the proposed coarse registration method based on the normal vector of feature points is helpful to the smooth completion of the subsequent fine registration process, avoids the phenomenon of falling into local optimization, and effectively completes the ski-resort point cloud registration.