Elevation moment of inertia: A new feature for monte Carlo localization in outdoor environment with elevation map

Tae Bum Kwon, Jae-Bok Song, Sang Hyun Joo

Research output: Contribution to journalArticle

7 Citations (Scopus)


The elevation map is one of themost popular maps for outdoor navigation.We propose the elevation moment of inertia (EMOI), which represents the distribution of elevation around a robot in an elevation map, for use in the matching of elevation maps. Using this feature, outdoor localization can be performed with an elevation map without external positioning systems. In this research, the Monte Carlo localization (MCL) method is used for outdoor localization, and the conventional method is based on range matching, which compares range sensor data with the range data predicted from an elevation map. Our proposed method is based on EMOI matching. The EMOI around a robot is compared with the EMOIs for all cells of the pregiven reference elevation map to find a robot pose with respect to the reference map. MCL based on EMOI matching is very fast, although its accuracy is slightly lower than that of conventional range matching. To deal with the disadvantage of EMOI matching, an adaptive switching scheme between EMOI matching and range matching was also proposed. Various outdoor experiments indicated that the proposed EMOI significantly reduced the convergence time of MCL. Therefore, the proposed feature is considered to be useful when an elevation map is used for outdoor localization.

Original languageEnglish
Pages (from-to)371-386
Number of pages16
JournalJournal of Field Robotics
Issue number3
Publication statusPublished - 2010 May 1


ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications

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