Abstract: Most existing methods for detecting plane in point cloud cost long operation time, and the result of detection is susceptible to noise. To address these problems, this paper put forward a kind of multi-plane detection algorithm based on geometric statistical characteristics of the point clouds. The proposed method coarsely segmented point clouds according to the change rate of the volume density firstly, then used the Multi-RANSAC to fit planes, at last the authors proposed a new merge-constraint condition to combine and optimize the initial fitted planes. The experimental results show that the method in this paper is easy to realize, can effectively reduce the influence of cumulative noise to the detection results, improve the plane detection accuracy and also greatly reduce the computing time.

Key words: plane detection, point cloud segmentation, RANdom SAmple Consensus (RANSAC), plane fitting, volume density

摘要： 针对以往点云多平面检测算法运算时间长、检测结果的准确性易受噪声影响这一问题，提出了一种基于点云几何统计特征的多平面检测算法。该方法首先根据体密度变化率对点云进行粗分割，然后利用多元随机抽样一致性算法(Multi-RANSAC)进行多平面拟合，最后提出了一种新的合并约束条件对拟合的初始平面进行优化合并。实验结果证明，该算法易于实现，能有效减少累积噪声对检测结果的影响，提高平面检测的正确率，极大地减少了计算时间开销。

关键词: 平面检测, 点云分割, 随机抽样一致性算法, 平面拟合, 体密度