Multi-robot SLAM for large scale map building using relative information of local maps

Takaaki Kojima, Yoshihiro Okawa, Toru Namerikawa

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

This paper deals with Multi-Robot SLAM for large scale map building. Specifically, each robot estimates a local map using EKF, and we merge these local maps into a global map. In this paper, we provide a new RLS based algorithm for map merging. First, we transform local maps into relative information which is considered as measurements for the global map. Then, we update the state estimate by RLS considering the weighting of measurements, which is determined by error propagation from the EKF SLAM. We prove the convergence of the error covariance matrix in this algorithm. In experimental results, we confirm the validity of the proposed algorithm and correctness of derived theorems for the convergence.

Original languageEnglish
Pages164-169
Number of pages6
Publication statusPublished - 2013 Jan 1
Event2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013 - Nagoya, Japan
Duration: 2013 Sep 142013 Sep 17

Other

Other2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013
CountryJapan
CityNagoya
Period13/9/1413/9/17

Keywords

  • EKF SLAM
  • Map fusion
  • Multi-robot SLAM
  • RLS

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • Cite this

    Kojima, T., Okawa, Y., & Namerikawa, T. (2013). Multi-robot SLAM for large scale map building using relative information of local maps. 164-169. Paper presented at 2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013, Nagoya, Japan.