激光与光电子学进展, 2019, 56 (13): 131501, 网络出版: 2019-07-11
基于投影分布熵的地面激光点云自动配准方法 下载: 906次
Registration of Terrestrial Laser Scanning Data Based on Projection Distribution Entropy
机器视觉 点云配准 地面激光扫描 信息熵 二维投影 machine vision point cloud registration terrestrial laser scanning information entropy two-dimensional projection
摘要
点云配准是地面三维激光扫描数据处理的重要环节。面向地形起伏较小的场景,提出了一种基于投影分布熵的地面激光点云自动配准方法,利用信息熵对点云投影分布的集中程度进行描述,并寻找点云间的最佳分布进行粗配准,以此作为迭代最邻近点算法的初始值进行精配准。相对于基于特征的自动配准方法,该方法主要关注点云整体分布的一致性。实验表明,该方法具有较高的稳定性和成功率,尤其在点云场景出现较大视角变化或包含较多重复、对称结构时具有良好的配准结果。
Abstract
Point cloud registration is an important step in the processing of terrestrial three-dimensional laser scanning data. Aiming at the scene with small terrain fluctuation, we propose an automatic point cloud registration method based on projection distribution entropy. Initially, information entropy is used to describe the intensity of point cloud projection distribution. Following this, a coarse registration is achieved by seeking an optimal point cloud distribution between two point clouds. Consequently, the transformation parameters are determined between the two point clouds with different distributions and supplied as an input to the iterative closest point algorithm to achieve a fine registration. Compared with the automatic point cloud registration method based on features, the proposed method's main concern is the consistency of the overall distributions of the clouds. Results show that the proposed method shows a robust and accurate registration outcome, especially for the point cloud scene with great change of perspective and multiple repetitive symmetrical structures.
梁建国, 陈茂霖, 马红. 基于投影分布熵的地面激光点云自动配准方法[J]. 激光与光电子学进展, 2019, 56(13): 131501. Jianguo Liang, Maolin Chen, Hong Ma. Registration of Terrestrial Laser Scanning Data Based on Projection Distribution Entropy[J]. Laser & Optoelectronics Progress, 2019, 56(13): 131501.