VINS on wheels

Kejian J. Wu, Chao X. Guo, Georgios Georgiou, Stergios I. Roumeliotis

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

27 Scopus citations

Abstract

In this paper, we present a vision-aided inertial navigation system (VINS) for localizing wheeled robots. In particular, we prove that VINS has additional unobservable directions, such as the scale, when deployed on a ground vehicle that is constrained to move along straight lines or circular arcs. To address this limitation, we extend VINS to incorporate low-frequency wheel-encoder data, and show that the scale becomes observable. Furthermore, and in order to improve the localization accuracy, we introduce the manifold-(m)VINS that exploits the fact that the vehicle moves on an approximately planar surface. In our experiments, we first show the performance degradation of VINS due to special motions, and then demonstrate that by utilizing the additional sources of information, our system achieves significantly higher positioning accuracy, while operating in real-time on a commercial-grade mobile device.

Original languageEnglish (US)
Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5155-5162
Number of pages8
ISBN (Electronic)9781509046331
DOIs
StatePublished - Jul 21 2017
Event2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore
Duration: May 29 2017Jun 3 2017

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other2017 IEEE International Conference on Robotics and Automation, ICRA 2017
CountrySingapore
CitySingapore
Period5/29/176/3/17

Bibliographical note

Funding Information:
†K. J. Wu is with the Department of Electrical and Computer Engineering, Univ. of Minnesota, Minneapolis, USA. kejian@cs.umn.edu ‡C. X. Guo, G. Georgiou, and S. I. Roumeliotis are with the Department of Computer Science and Engineering, Univ. of Minnesota, Minneapolis, USA. {chaguo,georgiou,stergios}@cs.umn.edu This work was supported by the University of Minnesota and the National Science Foundation (IIS-1111638, IIS-1328722).

Publisher Copyright:
© 2017 IEEE.

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