Abstract: Excessive density of point clouds leads to data redundancy, making it difficult to calculate, store, and display point cloud data. Aiming at the problem of simplifying point clouds in LiDAR terrain scanning, this paper proposes a multi-factor segmentation point cloud simplification method. Firstly, on the basis of improving point cloud organization method, the variation coefficient weighting method is used to synthesize four traditional point cloud feature extraction factors to obtain the final comprehensive evaluation factor. Then feature points and non-feature points are distinguished by the final factor. Secondly, the improved octree is used to divide all the points into subsets based on their location and the point number, and determination on whether to retain some of the non-feature points are made according to the number of feature points in each subset. This method can evaluate and select the characteristics of the data more comprehensively and objectively to obtain the most representative points, and achieve more precise simplification. The experiment shows that the error of the results of the multi-factor segmentation method is 20% to 50% lower than that of other methods, and the accuracy is 5% to 70% more uniform in the overall test area, which proves that the proposed method is superior.

Key words: LiDAR point cloud, simplification algorithm, feature factor, improved octree, vari-ation coefficient weighting method