Expansion of the Matching Scope and High-speed and High-accuracy Matching of 3-D Dot Cloud Data based on Stage Processing

Abstract

This paper proposes a high-speed and high-accuracy matching method for 3-D dot cloud data, and a method of expanding the scope of the matching. The proposed algorithm is designed to reduce the matching cost of ICP (Iterative Closest Point) algorithm by reducing the number of data. When the number of the data is decreased, the accuracy of the matching tends to become lower. However, we change the dot cloud data varying in the reduction rate progressively as the matching process progresses, and perform the matching with high speed and high accuracy. In this research we propose two types of stage processing depending on the magnitude relationship between the number of online data and offline data. One is the movement point type where online data is larger than offline data, and the other is the reference point type. Moreover, we expand the scope of matching by changing the number of reference points for the search points. We name this k-nearest neighbor stage processing. Our proposed methods are combined in order to achieve high-speed and large scope matching, which result in two techniques; the movement point type k-nearest neighbor stage processing, and the reference point type k-nearest neighbor stage processing. We perform some experiments using real data measured by a laser ranger to verify the effectiveness of the proposed techniques.