Extraction of Surface Features from LiDAR Point Clouds Using Incremental Segmentation Strategy

Abstract

LiDAR (Light Detection and Ranging) point clouds are measurements of irregularly distributed points on scanned object surfaces acquired with airborne or terrestrial LiDAR systems. Feature extraction is the key to transform LiDAR data into spatial information. Surface features are dominant in most LiDAR data corresponding to scanned object surfaces. This paper proposes a general method to segment co-surface points. An incremental segmentation strategy is developed for the implementation, which comprises several algorithms and employs various criteria to gradually segment LiDAR point clouds into several levels. There are four operation steps. First, the proximity of point clouds is established as spatial indices defined in an octree-structured voxel space. Second, a connected-component labeling algorithm for voxels is applied for segmenting neighboring points. Third, coplanar points then can be segmented with the octree-based split-and-merge algorithm as plane features. Finally, combining neighboring plane features forms surface features. With respect to each step, processed LiDAR point clouds are segmented into organized points, neighboring point groups, coplanar point groups, and co-surface point groups. The proposed method enables an incremental retrieval and analysis of a large LiDAR dataset. Experiment results demonstrate the effectiveness of the segmentation algorithm in handling both airborne and terrestrial LiDAR data. The end results as well as the intermediate results of the segmentation may be useful for object modeling of different purposes using LiDAR data.