Comparison of Local Plane Fitting Methods for Range Data

Caihua Wang, Hideki Tanahashi, Hidekazu Hirayu, Yoshinori Niwa, Kazuhiko Yamamoto

Abstract: In this research, we introduce a reasonable noise model for range data which is obtained by a laser radar range finder, and derive two simple approximate solutions of optimal local plane fitting the range data under the noise model. We compare our methods with general least-squares based methods, such as Z-function fitting, the eigenvalue method, the maximum likelihood estimation method, and the renormalization method, an iterative method to obtain the optimal fitting of planes of range data under the noise model. All the methods are compared and evaluated using both synthetic range data and real range data with ground truth. From the experimental evaluation results, the proposed methods are shown to be effective, and the general least-squares-based methods are shown to be unsuitable for the assumed noise model.