Plane Fitting of Point Clouds with Robust LTS Initial Value

Abstract:

To fit a plane with point clouds containing massive outliers, a fitting method with robust initial value by least trimmed squares (LTS) was presented. It firstly used LTS to ascertain initial plane coefficients by random sampling. Meanwhile, the median of all absolute deviations was adopted as initial mean square error of unit weight. Then it carried out selecting weight iteration. This method kept the high efficiency of selecting weight iteration as well as maintained the robustness of LTS. The test results show that selecting weight iteration with LTS initial value can keep robust to deal with point clouds including massive outliers. Finally, the accurate plane can be fitted.