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JRM Vol.29 No.4 pp. 685-696
doi: 10.20965/jrm.2017.p0685
(2017)

Paper:

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**, and Masato Edahiro*

*Graduate School of Informatics, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan

**Institute of Innovation for Future Society, Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan

Received:
March 3, 2017
Accepted:
June 6, 2017
Published:
August 20, 2017
Keywords:
visual localization, field robotics, Tsukuba Challenge
Abstract
Robust and Accurate Monocular Vision-Based Localization in Outdoor Environments of Real-World Robot Challenge

Visual localization within metric point cloud map

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%.

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Last updated on Nov. 10, 2017