Robust and accurate monocular vision-based localization in outdoor environments of real-world robot challenge

Adi Sujiwo, Eijiro Takeuchi, Luis Yoichi Morales, Naoki Akai, Hatem Darweesh, Yoshiki Ninomiya, Masato Edahiro

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

This paper describes our approach to perform robust monocular camera metric localization in the dynamic environments of Tsukuba Challenge 2016. We address two issues related to vision-based navigation. First, we improved the coverage by building a custom vocabulary out of the scene and improving upon place recognition routine which is key for global localization. Second, we established possibility of lifelong localization by using previous year’s map. Experimental results show that localization coverage was higher than 90% for six different data sets taken in different years, while localization average errors were under 0.2 m. Finally, the average of coverage for data sets tested with maps taken in different years was of 75%.

Original languageEnglish
Pages (from-to)685-696
Number of pages12
JournalJournal of Robotics and Mechatronics
Volume29
Issue number4
DOIs
Publication statusPublished - 2017 Aug
Externally publishedYes

Keywords

  • Field robotics
  • Tsukuba Challenge
  • Visual localization

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

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