IEICE TRANSACTIONS on Information
High-Precision Mobile Robot Localization Using the Integration of RAR and AKF
Summary :
The high-precision indoor positioning technology has gradually become one of the research hotspots in indoor mobile robots. Relax and Recover (RAR) is an indoor positioning algorithm using distance observations. The algorithm restores the robot's trajectory through curve fitting and does not require time synchronization of observations. The positioning can be successful with few observations. However, the algorithm has the disadvantages of poor resistance to gross errors and cannot be used for real-time positioning. In this paper, while retaining the advantages of the original algorithm, the RAR algorithm is improved with the adaptive Kalman filter (AKF) based on the innovation sequence to improve the anti-gross error performance of the original algorithm. The improved algorithm can be used for real-time navigation and positioning. The experimental validation found that the improved algorithm has a significant improvement in accuracy when compared to the original RAR. When comparing to the extended Kalman filter (EKF), the accuracy is also increased by 12.5%, which can be used for high-precision positioning of indoor mobile robots.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.1001-1009
- Publication Date
- 2023/05/01
- Publicized
- 2023/01/24
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022EDP7156
- Type of Manuscript
- PAPER
- Category
- Information Network
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Chen WANG, Hong TAN, "High-Precision Mobile Robot Localization Using the Integration of RAR and AKF" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 1001-1009, May 2023, doi: 10.1587/transinf.2022EDP7156.
Abstract: The high-precision indoor positioning technology has gradually become one of the research hotspots in indoor mobile robots. Relax and Recover (RAR) is an indoor positioning algorithm using distance observations. The algorithm restores the robot's trajectory through curve fitting and does not require time synchronization of observations. The positioning can be successful with few observations. However, the algorithm has the disadvantages of poor resistance to gross errors and cannot be used for real-time positioning. In this paper, while retaining the advantages of the original algorithm, the RAR algorithm is improved with the adaptive Kalman filter (AKF) based on the innovation sequence to improve the anti-gross error performance of the original algorithm. The improved algorithm can be used for real-time navigation and positioning. The experimental validation found that the improved algorithm has a significant improvement in accuracy when compared to the original RAR. When comparing to the extended Kalman filter (EKF), the accuracy is also increased by 12.5%, which can be used for high-precision positioning of indoor mobile robots.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDP7156/_p
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@ARTICLE{e106-d_5_1001,
author={Chen WANG, Hong TAN, },
journal={IEICE TRANSACTIONS on Information},
title={High-Precision Mobile Robot Localization Using the Integration of RAR and AKF},
year={2023},
volume={E106-D},
number={5},
pages={1001-1009},
abstract={The high-precision indoor positioning technology has gradually become one of the research hotspots in indoor mobile robots. Relax and Recover (RAR) is an indoor positioning algorithm using distance observations. The algorithm restores the robot's trajectory through curve fitting and does not require time synchronization of observations. The positioning can be successful with few observations. However, the algorithm has the disadvantages of poor resistance to gross errors and cannot be used for real-time positioning. In this paper, while retaining the advantages of the original algorithm, the RAR algorithm is improved with the adaptive Kalman filter (AKF) based on the innovation sequence to improve the anti-gross error performance of the original algorithm. The improved algorithm can be used for real-time navigation and positioning. The experimental validation found that the improved algorithm has a significant improvement in accuracy when compared to the original RAR. When comparing to the extended Kalman filter (EKF), the accuracy is also increased by 12.5%, which can be used for high-precision positioning of indoor mobile robots.},
keywords={},
doi={10.1587/transinf.2022EDP7156},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - High-Precision Mobile Robot Localization Using the Integration of RAR and AKF
T2 - IEICE TRANSACTIONS on Information
SP - 1001
EP - 1009
AU - Chen WANG
AU - Hong TAN
PY - 2023
DO - 10.1587/transinf.2022EDP7156
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - The high-precision indoor positioning technology has gradually become one of the research hotspots in indoor mobile robots. Relax and Recover (RAR) is an indoor positioning algorithm using distance observations. The algorithm restores the robot's trajectory through curve fitting and does not require time synchronization of observations. The positioning can be successful with few observations. However, the algorithm has the disadvantages of poor resistance to gross errors and cannot be used for real-time positioning. In this paper, while retaining the advantages of the original algorithm, the RAR algorithm is improved with the adaptive Kalman filter (AKF) based on the innovation sequence to improve the anti-gross error performance of the original algorithm. The improved algorithm can be used for real-time navigation and positioning. The experimental validation found that the improved algorithm has a significant improvement in accuracy when compared to the original RAR. When comparing to the extended Kalman filter (EKF), the accuracy is also increased by 12.5%, which can be used for high-precision positioning of indoor mobile robots.
ER -
English
