VINS on wheels

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

doi:10.1109/ICRA.2017.7989603

VINS on wheels

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

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

103 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 language	English (US)
Title of host publication	ICRA 2017 - IEEE International Conference on Robotics and Automation
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5155-5162
Number of pages	8
ISBN (Electronic)	9781509046331
DOIs	https://doi.org/10.1109/ICRA.2017.7989603
State	Published - Jul 21 2017
Event	2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore Duration: May 29 2017 → Jun 3 2017

Publication series

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

Other

Other	2017 IEEE International Conference on Robotics and Automation, ICRA 2017
Country/Territory	Singapore
City	Singapore
Period	5/29/17 → 6/3/17

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.

Publisher link

10.1109/ICRA.2017.7989603

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VINS on wheels. / Wu, Kejian J.; Guo, Chao X.; Georgiou, Georgios et al.
ICRA 2017 - IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc., 2017. p. 5155-5162 7989603 (Proceedings - IEEE International Conference on Robotics and Automation).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wu, KJ, Guo, CX, Georgiou, G & Roumeliotis, SI 2017, VINS on wheels. in ICRA 2017 - IEEE International Conference on Robotics and Automation., 7989603, Proceedings - IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers Inc., pp. 5155-5162, 2017 IEEE International Conference on Robotics and Automation, ICRA 2017, Singapore, Singapore, 5/29/17. https://doi.org/10.1109/ICRA.2017.7989603

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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.",

author = "Wu, {Kejian J.} and Guo, {Chao X.} and Georgios Georgiou and Roumeliotis, {Stergios I.}",

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: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Robotics and Automation, ICRA 2017 ; Conference date: 29-05-2017 Through 03-06-2017",

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N2 - 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.

AB - 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.

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VINS on wheels

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