Visual-inertial odometry using iterated cubature Kalman filter

Jianhua Xu; Huan Yu; Rui Teng

doi:10.1109/CCDC.2018.8407789

Visual-inertial odometry using iterated cubature Kalman filter

Jianhua Xu, Huan Yu, Rui Teng

School of Automation

Beijing Institute of Technology

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

2 Citations (Scopus)

Abstract

In recent years, there are many excellent algorithms for visual-inertial odometry. However, in practical engineering application, we must consider the computational cost. High-precision optimization-based methods usually can not meet the requirement of real-time. In this paper, we proposed a visual-inertial odometry algorithm, which is based on iterated cubature Kalman filter. Compared with EKF-based method, it can reduce the influence of linearization and improve localization precision. The computational complexity of this method is similar with other Kaiman filtering based methods. Experimental results are presented for a real-world dataset captured on a Beijing street with a land vehicle and the results show that the method proposed can attain a better accuracy than other methods.

Original language	English
Title of host publication	Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3837-3841
Number of pages	5
ISBN (Electronic)	9781538612439
DOIs	https://doi.org/10.1109/CCDC.2018.8407789
Publication status	Published - 6 Jul 2018
Event	30th Chinese Control and Decision Conference, CCDC 2018 - Shenyang, China Duration: 9 Jun 2018 → 11 Jun 2018

Publication series

Name	Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018

Conference

Conference	30th Chinese Control and Decision Conference, CCDC 2018
Country/Territory	China
City	Shenyang
Period	9/06/18 → 11/06/18

Keywords

Cubature Kalman filter
Localization
Visual-inertial Odometry

Access to Document

10.1109/CCDC.2018.8407789

Cite this

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title = "Visual-inertial odometry using iterated cubature Kalman filter",

abstract = "In recent years, there are many excellent algorithms for visual-inertial odometry. However, in practical engineering application, we must consider the computational cost. High-precision optimization-based methods usually can not meet the requirement of real-time. In this paper, we proposed a visual-inertial odometry algorithm, which is based on iterated cubature Kalman filter. Compared with EKF-based method, it can reduce the influence of linearization and improve localization precision. The computational complexity of this method is similar with other Kaiman filtering based methods. Experimental results are presented for a real-world dataset captured on a Beijing street with a land vehicle and the results show that the method proposed can attain a better accuracy than other methods.",

keywords = "Cubature Kalman filter, Localization, Visual-inertial Odometry",

author = "Jianhua Xu and Huan Yu and Rui Teng",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 30th Chinese Control and Decision Conference, CCDC 2018 ; Conference date: 09-06-2018 Through 11-06-2018",

year = "2018",

month = jul,

day = "6",

doi = "10.1109/CCDC.2018.8407789",

language = "English",

series = "Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3837--3841",

booktitle = "Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018",

address = "United States",

}

Xu, J, Yu, H & Teng, R 2018, Visual-inertial odometry using iterated cubature Kalman filter. in Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018. Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 3837-3841, 30th Chinese Control and Decision Conference, CCDC 2018, Shenyang, China, 9/06/18. https://doi.org/10.1109/CCDC.2018.8407789

Visual-inertial odometry using iterated cubature Kalman filter. / Xu, Jianhua; Yu, Huan; Teng, Rui.
Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 3837-3841 (Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018).

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

TY - GEN

T1 - Visual-inertial odometry using iterated cubature Kalman filter

AU - Xu, Jianhua

AU - Yu, Huan

AU - Teng, Rui

PY - 2018/7/6

Y1 - 2018/7/6

N2 - In recent years, there are many excellent algorithms for visual-inertial odometry. However, in practical engineering application, we must consider the computational cost. High-precision optimization-based methods usually can not meet the requirement of real-time. In this paper, we proposed a visual-inertial odometry algorithm, which is based on iterated cubature Kalman filter. Compared with EKF-based method, it can reduce the influence of linearization and improve localization precision. The computational complexity of this method is similar with other Kaiman filtering based methods. Experimental results are presented for a real-world dataset captured on a Beijing street with a land vehicle and the results show that the method proposed can attain a better accuracy than other methods.

AB - In recent years, there are many excellent algorithms for visual-inertial odometry. However, in practical engineering application, we must consider the computational cost. High-precision optimization-based methods usually can not meet the requirement of real-time. In this paper, we proposed a visual-inertial odometry algorithm, which is based on iterated cubature Kalman filter. Compared with EKF-based method, it can reduce the influence of linearization and improve localization precision. The computational complexity of this method is similar with other Kaiman filtering based methods. Experimental results are presented for a real-world dataset captured on a Beijing street with a land vehicle and the results show that the method proposed can attain a better accuracy than other methods.

KW - Cubature Kalman filter

KW - Localization

KW - Visual-inertial Odometry

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DO - 10.1109/CCDC.2018.8407789

M3 - Conference contribution

AN - SCOPUS:85050889153

T3 - Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018

SP - 3837

EP - 3841

BT - Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 30th Chinese Control and Decision Conference, CCDC 2018

Y2 - 9 June 2018 through 11 June 2018

ER -

Visual-inertial odometry using iterated cubature Kalman filter

Abstract

Publication series

Conference

Keywords

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