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Probabilistic map merging for multi-robot RBPF-SLAM with unknown initial poses

Published online by Cambridge University Press:  06 June 2011

Heon-Cheol Lee*
Affiliation:
School of Electrical Engineering and Computer Sciences, Seoul National University, Seoul, Korea
Seung-Hwan Lee
Affiliation:
School of Electrical Engineering and Computer Sciences, Seoul National University, Seoul, Korea
Myoung Hwan Choi
Affiliation:
Department of Electrical and Electronic Engineering, Kangwon National University, Gangwon-do, Korea
Beom-Hee Lee
Affiliation:
School of Electrical Engineering and Computer Sciences, Seoul National University, Seoul, Korea
*
*Corresponding author. Email: restore98@snu.ac.kr

Summary

This paper addresses the map merging problem, which is the most important issue in multi-robot simultaneous localization and mapping (SLAM) using the Rao–Blackwellized particle filter (RBPF-SLAM) with unknown initial poses. The map merging is performed using the map transformation matrix and the pair of map merging bases (MMBs) of the robots. However, it is difficult to find appropriate MMBs because each robot pose is estimated under multi-hypothesis in the RBPF-SLAM. In this paper, probabilistic map merging (PMM) using the Gaussian process is proposed to solve the problem. The performance of PMM was verified by reducing errors in the merged map with computer simulations and real experiments.

Type
Articles
Copyright
Copyright © Cambridge University Press 2011

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