An improved RANSAC algorithm for building point clouds segmentation in consideration of roof structure
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摘要: 针对传统RANSAC点云分割算法在处理多层次、多面片的复杂建筑物中的困难,提出一种改进算法对建筑物点云进行分割和几何基元的提取.首先,结合基于坡度和高差的三角形区域生长方法,对复杂建筑物的不同结构层次进行分解,提高了随机采样时的有效模型命中率,并降低了错分现象;然后,提出一种浮动一致集阈值的RANSAC算法,通过自动调整RANSAC算法中的关键参数,使算法能够适应不同尺度的几何基元.实验证明了该算法在复杂建筑物点云数据分割效果和运算效率上的有效性.Abstract: An improved RANSAC algorithm was proposed for point cloud segmentation and geometric primitives extraction of buildings with multiple facets and complex roof structures, including two innovations. Firstly, the"split-segment" strategy combined with regional growth concept is proposed to improve the segment result and efficiency of classic RANSAC algorithm; Secondly, an improved RANSAC algorithm with variant consensus set threshold is presented. By automatically adjusting the consensus set threshold value, geometric primitives with scale difference are likely to meet the validity test, thus avoiding the over -segmentation and under - segmentation problems of classic RANSAC algorithm with fixed consensus set threshold.
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Key words:
- LiDAR /
- regional growing /
- RANSAC /
- building /
- point clouds segmentation
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