Hybrid Extended Kalman Filter-based localization with a highly accurate odometry model of a mobile robot

Tran Him Cong, Young Joong Kim, Myo Taeg Lim

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

11 Citations (Scopus)

Abstract

This paper describes an improving method for solving localization problems with a highly accurate model of a mobile robot either in an uncertainly large-scale environment. Firstly, we motivate our approach by analyzing intensively the dead-reckoning model for the tricycle robot type. Secondly, we propose the localization algorithm based on a Hybrid Extended Kalman Filter using artificial beacons. In this paper, 360° sensor scan is used for each observation and the odometry data is updated to estimate the robot position. Then a comparison between the real and the estimated location of beacons and analyzing of the filter's performance are taken. The simulation results show that the proposed algorithm can lead the robot to robustly navigate in uncertain environments.

Original languageEnglish
Title of host publication2008 International Conference on Control, Automation and Systems, ICCAS 2008
Pages738-743
Number of pages6
DOIs
Publication statusPublished - 2008 Dec 1
Event2008 International Conference on Control, Automation and Systems, ICCAS 2008 - Seoul, Korea, Republic of
Duration: 2008 Oct 142008 Oct 17

Other

Other2008 International Conference on Control, Automation and Systems, ICCAS 2008
CountryKorea, Republic of
CitySeoul
Period08/10/1408/10/17

Fingerprint

Extended Kalman filters
Mobile robots
Robots
Sensors

Keywords

  • Extended Kalman filter
  • Localization
  • Mobile robot

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Cong, T. H., Kim, Y. J., & Lim, M. T. (2008). Hybrid Extended Kalman Filter-based localization with a highly accurate odometry model of a mobile robot. In 2008 International Conference on Control, Automation and Systems, ICCAS 2008 (pp. 738-743). [4694596] https://doi.org/10.1109/ICCAS.2008.4694596

Hybrid Extended Kalman Filter-based localization with a highly accurate odometry model of a mobile robot. / Cong, Tran Him; Kim, Young Joong; Lim, Myo Taeg.

2008 International Conference on Control, Automation and Systems, ICCAS 2008. 2008. p. 738-743 4694596.

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

Cong, TH, Kim, YJ & Lim, MT 2008, Hybrid Extended Kalman Filter-based localization with a highly accurate odometry model of a mobile robot. in 2008 International Conference on Control, Automation and Systems, ICCAS 2008., 4694596, pp. 738-743, 2008 International Conference on Control, Automation and Systems, ICCAS 2008, Seoul, Korea, Republic of, 08/10/14. https://doi.org/10.1109/ICCAS.2008.4694596
Cong TH, Kim YJ, Lim MT. Hybrid Extended Kalman Filter-based localization with a highly accurate odometry model of a mobile robot. In 2008 International Conference on Control, Automation and Systems, ICCAS 2008. 2008. p. 738-743. 4694596 https://doi.org/10.1109/ICCAS.2008.4694596
Cong, Tran Him ; Kim, Young Joong ; Lim, Myo Taeg. / Hybrid Extended Kalman Filter-based localization with a highly accurate odometry model of a mobile robot. 2008 International Conference on Control, Automation and Systems, ICCAS 2008. 2008. pp. 738-743
@inproceedings{9d3e8f481b9242a8856905daddc16c0e,
title = "Hybrid Extended Kalman Filter-based localization with a highly accurate odometry model of a mobile robot",
abstract = "This paper describes an improving method for solving localization problems with a highly accurate model of a mobile robot either in an uncertainly large-scale environment. Firstly, we motivate our approach by analyzing intensively the dead-reckoning model for the tricycle robot type. Secondly, we propose the localization algorithm based on a Hybrid Extended Kalman Filter using artificial beacons. In this paper, 360° sensor scan is used for each observation and the odometry data is updated to estimate the robot position. Then a comparison between the real and the estimated location of beacons and analyzing of the filter's performance are taken. The simulation results show that the proposed algorithm can lead the robot to robustly navigate in uncertain environments.",
keywords = "Extended Kalman filter, Localization, Mobile robot",
author = "Cong, {Tran Him} and Kim, {Young Joong} and Lim, {Myo Taeg}",
year = "2008",
month = "12",
day = "1",
doi = "10.1109/ICCAS.2008.4694596",
language = "English",
isbn = "9788995003893",
pages = "738--743",
booktitle = "2008 International Conference on Control, Automation and Systems, ICCAS 2008",

}

TY - GEN

T1 - Hybrid Extended Kalman Filter-based localization with a highly accurate odometry model of a mobile robot

AU - Cong, Tran Him

AU - Kim, Young Joong

AU - Lim, Myo Taeg

PY - 2008/12/1

Y1 - 2008/12/1

N2 - This paper describes an improving method for solving localization problems with a highly accurate model of a mobile robot either in an uncertainly large-scale environment. Firstly, we motivate our approach by analyzing intensively the dead-reckoning model for the tricycle robot type. Secondly, we propose the localization algorithm based on a Hybrid Extended Kalman Filter using artificial beacons. In this paper, 360° sensor scan is used for each observation and the odometry data is updated to estimate the robot position. Then a comparison between the real and the estimated location of beacons and analyzing of the filter's performance are taken. The simulation results show that the proposed algorithm can lead the robot to robustly navigate in uncertain environments.

AB - This paper describes an improving method for solving localization problems with a highly accurate model of a mobile robot either in an uncertainly large-scale environment. Firstly, we motivate our approach by analyzing intensively the dead-reckoning model for the tricycle robot type. Secondly, we propose the localization algorithm based on a Hybrid Extended Kalman Filter using artificial beacons. In this paper, 360° sensor scan is used for each observation and the odometry data is updated to estimate the robot position. Then a comparison between the real and the estimated location of beacons and analyzing of the filter's performance are taken. The simulation results show that the proposed algorithm can lead the robot to robustly navigate in uncertain environments.

KW - Extended Kalman filter

KW - Localization

KW - Mobile robot

UR - http://www.scopus.com/inward/record.url?scp=58149102584&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=58149102584&partnerID=8YFLogxK

U2 - 10.1109/ICCAS.2008.4694596

DO - 10.1109/ICCAS.2008.4694596

M3 - Conference contribution

AN - SCOPUS:58149102584

SN - 9788995003893

SP - 738

EP - 743

BT - 2008 International Conference on Control, Automation and Systems, ICCAS 2008

ER -