SLAM by combining multidimensional scaling and particle filtering

Abstract

This paper presents an algorithm for the simultaneous localization and mapping (SLAM) problem. Inspired by the basic idea of the FastSLAM which separates the robot pose estimation problem and mapping problem, we use the particle filter (PF) to estimate the pose of individual robot and use the multidimensional scaling (MDS), one of the distance mapping method, to find the relative coordinates of landmarks toward the robot. We apply the proposed algorithm to not only the single robot SLAM, but also the multi-robot SLAM. Experimental results demonstrate the effectiveness of the proposed algorithm over the FastSLAM. © 2008 Springer-Verlag Berlin Heidelberg.