A geometric histogram method for accurate and robust motion estimation from range data

Motion estimation from outlier corrupted data is a fundamental and difficult problem acknowledged in the machine vision literature. In this paper a robust motion estimation method is presented. First, the Monte Carlo resampling technique is used for an initial estimation of motion parameters, then a geometric histogram method is proposed to synthesize possible solutions to motion parameters based on geometric properties of reflected correspondence vectors. A number of experiments using both synthetic data and real images demonstrate the robustness of the proposed motion estimation method leading to more accurate registration of free form shapes