Internet of Things and Cloud Computing
Volume 5, Issue 3, June 2017, Pages: 52-58
Received: Aug. 1, 2017;
Published: Aug. 2, 2017
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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
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.
An Indoor Mobile Robot Localization Method Based on Information Fusion, Internet of Things and Cloud Computing.
Vol. 5, No. 3,
2017, pp. 52-58.
Eslim L M, Ibrahim W M, Hassanein H S. GOSSIPY: A distributed localization system for Internet of Things using RFID technology [C]. GLOBECOM 2013 - 2013 IEEE Global Communications Conference. IEEE, 2013, pp. 140-145.
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.
Zhao Y, Liu Y, Ni L M. VIRE: Active RFID-based Localization Using Virtual Reference Elimination [C]. International Conference on Parallel Processing. IEEE Computer Society, 2007, pp. 56-62.
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.
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.
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.
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.
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.
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.
Al-Najjar Y. Comparison of Image Quality Assessment: PSNR, HVS, SSIM, UIQI [J]. International Journal of Scientific & Engineering Research, 2012, 3 (3).
Wang c, Shang Y Y Ding H, et al. Licence plate recognition algorithm research based on SSIM [J]. Optical technique, 2013, 39 (6), pp. 505-509.
Welch G, Bishop G. An Introduction to the Kalman Filter [J]. University of North Carolina at Chapel Hill, 1995 (7), pp. 127-132.
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.
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.