In this paper, we propose an improved method of point cloud model taking into account the geometric constraint. We first apply robust least-squares to enhance the initial value’s accuracy, and then use penalty function method to transform constrained optimization problems into unconstrained optimization problems, and finally solve the problems using the L-M algorithm. The experimental results show that the internal accuracy is improved, and it is shown that the improved method for point clouds modeling proposed by this paper outperforms the traditional point clouds modeling methods.
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JIanping Yue, Yi Pan, Shun Yue, Dapeng Liu, Bin Liu, Nan Huang, "A point cloud modeling method based on geometric constraints mixing the robust least squares method," Proc. SPIE 10006, Lidar Technologies, Techniques, and Measurements for Atmospheric Remote Sensing XII, 100060M (24 October 2016);