Observation likelihood model design and failure recovery scheme toward reliable localization of mobile robots

Chang Bae Moon, Woojin Chung, Nakju Lett Doh

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Although there have been many researches on mobile robot localization, it is still difficult to obtain reliable localization performance in a human co-existing real environment. Reliability of localization is highly dependent upon developer's experiences because uncertainty is caused by a variety of reasons. We have developed a range sensor based integrated localization scheme for various indoor service robots. Through the experience, we found out that there are several significant experimental issues. In this paper, we provide useful solutions for following questions which are frequently faced with in practical applications: 1) How to design an observation likelihood model? 2) How to detect the localization failure? 3) How to recover from the localization failure? We present design guidelines of observation likelihood model. Localization failure detection and recovery schemes are presented by focusing on abrupt wheel slippage. Experiments were carried out in a typical office building environment. The proposed scheme to identify the localizer status is useful in practical environments. Moreover, the semi-global localization is a computationally efficient recovery scheme from localization failure. The results of experiments and analysis clearly present the usefulness of proposed solutions.

Original languageEnglish
Pages (from-to)113-122
Number of pages10
JournalInternational Journal of Advanced Robotic Systems
Issue number4
Publication statusPublished - 2010 Dec


  • Mobile robot localization
  • Monte Carlo Localization (MCL)
  • Observation likelihood model
  • Wheel slippage
  • Wheeled mobile robot

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Artificial Intelligence


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