光学学报, 2020, 40 (23): 2310001, 网络出版: 2020-11-23   

平面特征约束下基于四元数描述的LiDAR点云配准算法 下载: 753次

Planar Feature-Constrained, Quaternion-Based Registration Algorithm for LiDAR Point Clouds
王永波 1,2,*郑南山 1,2卞正富 1,2
作者单位
1 中国矿业大学自然资源部国土环境与灾害监测重点实验室, 江苏 徐州 221116
2 中国矿业大学江苏省资源环境信息工程重点实验室, 江苏 徐州 221116
摘要
系统探讨基于平面特征约束的地面LiDAR点云的高精度融合问题,引入单位四元数作为空间旋转变换的描述算子,给出了三维空间中平面特征的四参数表达方法,在确保数学表达形式唯一的基础上实现对基于平面特征约束的空间相似变换模型的构建。以配准后同名平面特征的参数对等作为约束条件,基于最小二乘准则构建了三维空间相似变换的目标函数,并通过函数的极值化分析实现了平面特征约束下相邻测站LiDAR点云配准参数的迭代求解。最后,分别通过两组实测LiDAR点云数据对算法的正确性与有效性进行验证。结果表明:在求解空间相似变换参数的过程中,借助平面特征的四参数表达法,通过参数对等的条件约束来判断配准后同名特征的一致性,同时满足了同名平面特征之间的法向一致与距离为零两个约束条件;四元数的引入使空间相似变换模型的表达形式更加简洁,配准过程中的附加约束条件更少,在实验方案中,给定任意的未知参数初值,所提算法均能够运行并得到正确结果。
Abstract
The present work systematically discusses the planar-feature-based registration method of high-precision fusion of terrestrial LiDAR point clouds, wherein unit quaternion is used as the description operator of spatial rotation transformation. The 4-tuple representation method of planar features in three-dimensional (3D) space is given first. Then, the planar feature-based spatial similarity transformation model is constructed on the basis of ensuring the uniqueness of those planar features' mathematical expressions. Using the parameter equivalent of each conjugate planar features after registration as the constraint condition, the objective function of the 3D spatial similarity transformation is constructed according to the least square criterion, and the iterative solution of the registration parameters is analyzed according to the extremum of the function. Finally, the correctness and effectiveness of the algorithm are verified by two sets of LiDAR point cloud data. Results show that in solving the spatial similarity transformation parameters, the 4-tuple expression method of planar features is used in judging the consistency of the same-name features after registration through the condition constraints of the parameter equivalent. Simultaneously, the two constraints of normal consistency and distance zero between the same-name plane features are considered. Introducing quaternion makes the expressions of the spatial similarity transformation model more concise, and there are fewer additional constraints in during registration. In the experimental scheme, given any initial value of an unknown parameter, the proposed algorithm can run and get correct results.

王永波, 郑南山, 卞正富. 平面特征约束下基于四元数描述的LiDAR点云配准算法[J]. 光学学报, 2020, 40(23): 2310001. Yongbo Wang, Nanshan Zheng, Zhengfu Bian. Planar Feature-Constrained, Quaternion-Based Registration Algorithm for LiDAR Point Clouds[J]. Acta Optica Sinica, 2020, 40(23): 2310001.

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