Probabilistic map merging for multi-robot rbpf-slam with unknown initial poses

Authors:
Heon-cheol Lee;Seung-hwan Lee;Myoung hwan Choi;Beom-hee Lee
Affiliations:
School of electrical engineering and computer sciences, seoul national university, seoul, korea;School of electrical engineering and computer sciences, seoul national university, seoul, korea;Department of electrical and electronic engineering, kangwon national university, gangwon-do, korea;School of electrical engineering and computer sciences, seoul national university, seoul, korea
Venue:
Robotica
Year:
2012

Citing 6
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Abstract

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.