Testing the Accuracy of the Modified ICP Algorithm with Multimodal Weighting Factors

Abstract

SLAM technology is increasingly used to self-locate mobile robots in an unknown environment. One of the methods used in this technology is called scan matching. Increasing evidence is placed on the accuracy and speed of the methods used in terms of navigating mobile robots. The aim of this article is to present a modification to the standard method of Iterative Closest Point (ICP) environment scan matching using the authors’ three original weighting factors based on the error modeling. The presented modification was supported by a simulation study whose aim was not exclusively to check the effect of the factors but also to examine the effect of the number of points in scans on the correct and accurate development of the rotation matrix and the translation vector. The study demonstrated both an increase in the accuracy of ICP results following the implementation of the proposed modification and a noticeable increase in accuracy with an increase in the mapping device’s angular resolution. The proposed method has a positive impact on reducing number of iteration and computing time. The research results have shown to be promising and will be extended to 3D space in the future.