SLAM by Combining Multidimensional Scaling and Particle Filtering

Authors:
Hongmo Je;Daijin Kim
Affiliations:
Intelligent Multimedia Laboratory, Dept. of Computer Science & Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Korea;Intelligent Multimedia Laboratory, Dept. of Computer Science & Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Korea
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Year:
2008

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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.