HM-ICP: Fast 3-D Registration Algorithm with Hierarchical and Region Selection Approach of M-ICP

This paper presents a novel fast and highly accurate 3-D registration algorithm. The ICP (Iterative Closest Point) algorithm converges all the 3-D data points of two data sets to the best-matching points with minimum evaluation values. This algorithm is in widespread use because it has good validity for many applications, but it extracts a heavy computational cost and is very sensitive to error. This is because it uses all the data points of two data sets and least mean square optimization. We previously proposed the M-ICP algorithm, which uses M-estimation to realize robustness against outlying gross noise with the original ICP algorithm. In this paper, we propose a novel algorithm called HM-ICP (Hierarchical M-ICP), which is an extension of the M-ICP that selects regions for matching and hierarchical searching of selected regions. This method selects regions by evaluating the variance of distance values in the target region, and homogeneous topological mapping. Some fundamental experiments using real data sets of 3-D measurement demonstrate the effectiveness of the proposed method, achieving a reduction of more than ten thousand times for computational costs. We also confirmed an error of less than 0.1% for the measurement distance.