基于透视强度影像特征的点云配准算法

针对大规模测绘项目,靶标的布设和定位通常耗费大量人力。本文提出基于透视强度影像特征的点云配准算法,对于站—站配准,首先利用点云数据生成待配准点云的透视强度影像;其次,从强度影像中提取角点,通过特征匹配的方法确定同名点,并将同名点的像点坐标转换为物方三维坐标;最后,依据同名点的三维坐标,基于奇异值分解方法估计站—站之间的配准参数,并在配准参数解算过程中使用随机抽样一致算法剔除同名点粗差。通过实验结果验证该方法的稳健性和有效性。

Point cloud registration algorithm based on the characteristics of perspective intensity image

For large-scale surveying and mapping projects,the placement and positioning of targets usually consume a lot of labor. This paper proposes a point cloud registration algorithm based on perspective intensity image features. For station-to-station registration,firstly,perspective intensity images of the point cloud to be registered are generated by exploiting point cloud data;secondly,we extract the corner points from the intensity image and determine the corresponding points through the feature matching method and convert the image point coordinates of the corresponding points into the object-side 3 D coordinates;Finally,according to the three-dimensional coordinates of the corresponding points,the registration parameters between the stations are estimated based on the singular value decomposition method,and a random sampling consensus algorithm is used to eliminate the gross error of the corresponding points during the solution of the registration parameters. The experimental results verify the robustness and effectiveness of the method.