An Indoor Mobile Robot Localization Method Based on Information Fusion

Yang Li; Qijin Ji; Yanqin Zhu

doi:doi:10.11648/j.iotcc.20170503.13

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An Indoor Mobile Robot Localization Method Based on Information Fusion

Yang Li, Qijin Ji, Yanqin Zhu

Published in Internet of Things and Cloud Computing (Volume 5, Issue 3)

Received: 1 August 2017 Accepted: Published: 2 August 2017

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Abstract

In order to meet the requirement of positioning accuracy of indoor mobile robot, an indoor localization method based on information fusion is proposed. Firstly，using the Radio frequency identification (RFID) location method to determine the approximate range of the mobile robot's position, in the scope of the current with visual positioning for robot pose information including location coordinates and the deflection Angle; Secondly, using adaptive weighted fusion method to fuse RFID and visual location information; finally, the final result is obtained by Kalman filtering on the location information. The experimental results show that this method can improve the precision of positioning effectively.

Published in	Internet of Things and Cloud Computing (Volume 5, Issue 3)
DOI	10.11648/j.iotcc.20170503.13
Page(s)	52-58
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Indoor Localization, RFID, Visual Retrieval, Information Fusion

References

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[2]	Ni L M, Liu Y, Lau Y C, et al. LANDMARC: indoor location sensing using active RFID [J]. Wireless Networks, 2004, 10 (6), pp. 407.
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[4]	Potgantwar A D, Wadhai V M. Location Based System for Mobile Devices with Integration of RFID and Wireless Technology-Issues and Proposed System [C]. International Conference on Process Automation, Control and Computing. IEEE, 2011, pp. 1-5.
[5]	Bonin-Font F, Ortiz A, Oliver G. Visual Navigation for Mobile Robots: A Survey [J]. Journal of Intelligent & Robotic Systems, 2008, 53 (3), pp. 263-296.
[6]	Wu C J, Tsai W H. Location estimation for indoor autonomous vehicle navigation by omni-directional vision using circular landmarks on ceilings [J]. Robotics & Autonomous Systems, 2009, 57 (5), pp. 546-555.
[7]	Wolf J, Burgard W, Burkhardt H. Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization [J]. IEEE Transactions on Robotics, 2005, 21 (2), pp. 208-216.
[8]	Wong S F, Ni X. Signal propagation model calibration under metal noise factor for indoor localization by using RFID [C]. IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, 2014, pp. 978-982.
[9]	Aso M, Saikawa T, Hattori T. Mobile station location estimation using the maximum likelihood method in sector cell systems [C]. Vehicular Technology Conference, 2002. Proceedings. Vtc 2002-Fall. 2002 IEEE. IEEE Xplore, 2002, pp. 1192-1196.
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[12]	Welch G, Bishop G. An Introduction to the Kalman Filter [J]. University of North Carolina at Chapel Hill, 1995 (7), pp. 127-132.
[13]	Li Q Y, Chu J K, Li R Q, et al. Moving object tracking algorithm for mobile robot based on Kalman filter [J]. Transducer and Microsystem Technologies, 2008, 27 (11), pp. 66-68.
[14]	Chung H Y, Hou C C, Chen Y S. Indoor Intelligent Mobile Robot Localization Using Fuzzy Compensation and Kalman Filter to Fuse the Data of Gyroscope and Magnetometer [J]. IEEE Transactions on Industrial Electronics, 2015, 62 (10)，pp. 6436-6447.

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APA Style

Yang Li, Qijin Ji, Yanqin Zhu. (2017). An Indoor Mobile Robot Localization Method Based on Information Fusion. Internet of Things and Cloud Computing, 5(3), 52-58. https://doi.org/10.11648/j.iotcc.20170503.13

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ACS Style

Yang Li; Qijin Ji; Yanqin Zhu. An Indoor Mobile Robot Localization Method Based on Information Fusion. Internet Things Cloud Comput. 2017, 5(3), 52-58. doi: 10.11648/j.iotcc.20170503.13

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AMA Style

Yang Li, Qijin Ji, Yanqin Zhu. An Indoor Mobile Robot Localization Method Based on Information Fusion. Internet Things Cloud Comput. 2017;5(3):52-58. doi: 10.11648/j.iotcc.20170503.13

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@article{10.11648/j.iotcc.20170503.13,
  author = {Yang Li and Qijin Ji and Yanqin Zhu},
  title = {An Indoor Mobile Robot Localization Method Based on Information Fusion},
  journal = {Internet of Things and Cloud Computing},
  volume = {5},
  number = {3},
  pages = {52-58},
  doi = {10.11648/j.iotcc.20170503.13},
  url = {https://doi.org/10.11648/j.iotcc.20170503.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20170503.13},
  abstract = {In order to meet the requirement of positioning accuracy of indoor mobile robot, an indoor localization method based on information fusion is proposed. Firstly，using the Radio frequency identification (RFID) location method to determine the approximate range of the mobile robot's position, in the scope of the current with visual positioning for robot pose information including location coordinates and the deflection Angle; Secondly, using adaptive weighted fusion method to fuse RFID and visual location information; finally, the final result is obtained by Kalman filtering on the location information. The experimental results show that this method can improve the precision of positioning effectively.},
 year = {2017}
}

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TY  - JOUR
T1  - An Indoor Mobile Robot Localization Method Based on Information Fusion
AU  - Yang Li
AU  - Qijin Ji
AU  - Yanqin Zhu
Y1  - 2017/08/02
PY  - 2017
N1  - https://doi.org/10.11648/j.iotcc.20170503.13
DO  - 10.11648/j.iotcc.20170503.13
T2  - Internet of Things and Cloud Computing
JF  - Internet of Things and Cloud Computing
JO  - Internet of Things and Cloud Computing
SP  - 52
EP  - 58
PB  - Science Publishing Group
SN  - 2376-7731
UR  - https://doi.org/10.11648/j.iotcc.20170503.13
AB  - In order to meet the requirement of positioning accuracy of indoor mobile robot, an indoor localization method based on information fusion is proposed. Firstly，using the Radio frequency identification (RFID) location method to determine the approximate range of the mobile robot's position, in the scope of the current with visual positioning for robot pose information including location coordinates and the deflection Angle; Secondly, using adaptive weighted fusion method to fuse RFID and visual location information; finally, the final result is obtained by Kalman filtering on the location information. The experimental results show that this method can improve the precision of positioning effectively.
VL  - 5
IS  - 3
ER  -

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Author Information

Yang Li

School of Computer Science and Technology, Soochow University, Suzhou, China
Qijin Ji

School of Computer Science and Technology, Soochow University, Suzhou, China
Yanqin Zhu

School of Computer Science and Technology, Soochow University, Suzhou, China

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APA Style

Yang Li, Qijin Ji, Yanqin Zhu. (2017). An Indoor Mobile Robot Localization Method Based on Information Fusion. Internet of Things and Cloud Computing, 5(3), 52-58. https://doi.org/10.11648/j.iotcc.20170503.13

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ACS Style

Yang Li; Qijin Ji; Yanqin Zhu. An Indoor Mobile Robot Localization Method Based on Information Fusion. Internet Things Cloud Comput. 2017, 5(3), 52-58. doi: 10.11648/j.iotcc.20170503.13

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AMA Style

Yang Li, Qijin Ji, Yanqin Zhu. An Indoor Mobile Robot Localization Method Based on Information Fusion. Internet Things Cloud Comput. 2017;5(3):52-58. doi: 10.11648/j.iotcc.20170503.13

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@article{10.11648/j.iotcc.20170503.13,
  author = {Yang Li and Qijin Ji and Yanqin Zhu},
  title = {An Indoor Mobile Robot Localization Method Based on Information Fusion},
  journal = {Internet of Things and Cloud Computing},
  volume = {5},
  number = {3},
  pages = {52-58},
  doi = {10.11648/j.iotcc.20170503.13},
  url = {https://doi.org/10.11648/j.iotcc.20170503.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20170503.13},
  abstract = {In order to meet the requirement of positioning accuracy of indoor mobile robot, an indoor localization method based on information fusion is proposed. Firstly，using the Radio frequency identification (RFID) location method to determine the approximate range of the mobile robot's position, in the scope of the current with visual positioning for robot pose information including location coordinates and the deflection Angle; Secondly, using adaptive weighted fusion method to fuse RFID and visual location information; finally, the final result is obtained by Kalman filtering on the location information. The experimental results show that this method can improve the precision of positioning effectively.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - An Indoor Mobile Robot Localization Method Based on Information Fusion
AU  - Yang Li
AU  - Qijin Ji
AU  - Yanqin Zhu
Y1  - 2017/08/02
PY  - 2017
N1  - https://doi.org/10.11648/j.iotcc.20170503.13
DO  - 10.11648/j.iotcc.20170503.13
T2  - Internet of Things and Cloud Computing
JF  - Internet of Things and Cloud Computing
JO  - Internet of Things and Cloud Computing
SP  - 52
EP  - 58
PB  - Science Publishing Group
SN  - 2376-7731
UR  - https://doi.org/10.11648/j.iotcc.20170503.13
AB  - In order to meet the requirement of positioning accuracy of indoor mobile robot, an indoor localization method based on information fusion is proposed. Firstly，using the Radio frequency identification (RFID) location method to determine the approximate range of the mobile robot's position, in the scope of the current with visual positioning for robot pose information including location coordinates and the deflection Angle; Secondly, using adaptive weighted fusion method to fuse RFID and visual location information; finally, the final result is obtained by Kalman filtering on the location information. The experimental results show that this method can improve the precision of positioning effectively.
VL  - 5
IS  - 3
ER  -

Copy | Download