Localization based on multiple visual-metric maps

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

This paper presents a fusion of monocular camera-based metric localization, IMU and odometry in dynamic environments of public roads. We build multiple vision-based maps and use them at the same time in localization phase. For the mapping phase, visual maps are built by employing ORB-SLAM and accurate metric positioning from LiDAR-based NDT scan matching. This external positioning is utilized to correct for scale drift inherent in all vision-based SLAM methods. Next in the localization phase, these embedded positions are used to estimate the vehicle pose in metric global coordinates using solely monocular camera. Furthermore, to increase system robustness we also proposed utilization of multiple maps and sensor fusion with odometry and IMU using particle filter method. Experimental testing were performed through public road environment as far as 170 km at different times of day to evaluate and compare localization results of vision-only, GNSS and sensor fusion methods. The results show that sensor fusion method offers lower average errors than GNSS and better coverage than vision-only one.

Original languageEnglish
Title of host publicationMFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages212-219
Number of pages8
ISBN (Electronic)9781509060641
DOIs
Publication statusPublished - 2017 Dec 7
Externally publishedYes
Event13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017 - Daegu, Korea, Republic of
Duration: 2017 Nov 162017 Nov 18

Publication series

NameIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Volume2017-November

Conference

Conference13th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017
CountryKorea, Republic of
CityDaegu
Period17/11/1617/11/18

Keywords

  • Robot Vision Systems
  • Simultaneous Localization and Mapping

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

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  • Cite this

    Sujiwo, A., Takeuchi, E., Morales, L. Y., Akai, N., Ninomiya, Y., & Edahiro, M. (2017). Localization based on multiple visual-metric maps. In MFI 2017 - 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (pp. 212-219). (IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems; Vol. 2017-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MFI.2017.8170431