A New Method of Ski Tracks Extraction Based on Laser Intensity Information

Abstract

At present, the mainstream laser point cloud classification algorithms are mainly based on the geometric information of the target. Nevertheless, if there is occlusion between the targets, the classification effect will be negatively affected. Compared with the above methods, a new method of ski tracks extraction using laser intensity information based on target reflection is presented in this paper. The method can complete the downsampling of the point cloud datasets of ski tracks under the condition that the information of the target edge is complete. Then, the clustering and extraction of ski tracks are effectively accomplished based on the smoothing threshold and curvature between adjacent point clouds. The experimental results show that, different from the traditional methods, the composite classification method based on the intensity information proposed in this paper can effectively extract ski tracks from the complex background. By comparing the proposed method to the Euclidean distance method, the clustering segmentation method, and the RANSAC method, the average extraction accuracy is increased by 16.9%, while the over extraction rate is reduced by 8.4% and the under extraction rate is reduced by 8.6%, allowing us to accurately extract the ski track point cloud of a ski resort.