Fusion of Wheel Encoded Data and RFID Signals using Kalman Filter for Robot Indoor Localization
DOI:
https://doi.org/10.62411/faith.3048-3719-126Keywords:
Extended Kalman Filter, Indoor Localization, Mobile Robot, RFID, Sensor FusionAbstract
Indoor positioning technology plays an important role in improving efficiency and automating industrial processes such as warehouse management, production lines, and mobile robot navigation. However, existing RFID-based and odometry-only localization methods still suffer from limited accuracy, drift, and dependence on predefined infrastructure. To address these challenges, this work proposes a lightweight sensor fusion framework that combines wheel encoder data and phase-based RFID signals using an Extended Kalman Filter (EKF) for accurate indoor localization of mobile robots. The proposed method does not require prior knowledge of the tag map and enables convergence even when the robot starts outside the reader's range. Simulation results demonstrate that the fused method achieves an average positioning error of 5.4 cm and a final error of less than 8 cm. An ablation study comparing odometry-only, RFID-only, and fusion scenarios confirms the superiority of the integrated approach in terms of accuracy and robustness. The system is suitable for real-time implementation in cost-effective embedded platforms and has potential for deployment in smart warehouses and logistics environments.
Downloads
References
C. Li et al., “ReLoc: Hybrid RSSI-and Phase-based Relative UHF-RFID Tag Localization with COTS Devices,” IEEE Trans. Instrum. Meas., pp. 1–1, 2020, doi: 10.1109/TIM.2020.2991564.
A. Tzitzis, A. Raptopoulos Chatzistefanou, T. V. Yioultsis, and A. G. Dimitriou, “A Real-Time Multi-Antenna SAR-Based Method for 3D Localization of RFID Tags by a Moving Robot,” IEEE J. Radio Freq. Identif., vol. 5, no. 2, pp. 207–221, Jun. 2021, doi: 10.1109/JRFID.2021.3070409.
A. Coboi, M. D. M. T. Nguyen, V. N. Pham, T. C. Vu, M. D. M. T. Nguyen, and D. T. Nguyen, “Zigbee Based Mobile Sensing for Wireless Sensor Networks,” Comput. Networks Commun., Dec. 2023, doi: 10.37256/cnc.1220233923.
S. Li, M. Hedley, K. Bengston, D. Humphrey, M. Johnson, and W. Ni, “Passive Localization of Standard WiFi Devices,” IEEE Syst. J., vol. 13, no. 4, pp. 3929–3932, Dec. 2019, doi: 10.1109/JSYST.2019.2903278.
M. T. Nguyen, L. H. Truong, T. T. Tran, and C.-F. Chien, “Artificial intelligence based data processing algorithm for video surveillance to empower industry 3.5,” Comput. Ind. Eng., vol. 148, p. 106671, Oct. 2020, doi: 10.1016/j.cie.2020.106671.
M. T. Nguyen, “An energy-efficient framework for multimedia data routing in Internet of Things (IoTs),” EAI Endorsed Trans. Ind. Networks Intell. Syst., vol. 6, no. 19, p. 159120, Jun. 2019, doi: 10.4108/eai.13-6-2019.159120.
J. A. del Peral-Rosado, R. Raulefs, J. A. Lopez-Salcedo, and G. Seco-Granados, “Survey of Cellular Mobile Radio Localization Methods: From 1G to 5G,” IEEE Commun. Surv. Tutorials, vol. 20, no. 2, pp. 1124–1148, 2018, doi: 10.1109/COMST.2017.2785181.
A. Mittal, S. Tiku, and S. Pasricha, “Adapting Convolutional Neural Networks for Indoor Localization with Smart Mobile Devices,” in Proceedings of the 2018 Great Lakes Symposium on VLSI, May 2018, pp. 117–122. doi: 10.1145/3194554.3194594.
A. Parr, R. Miesen, and M. Vossiek, “Comparison of Phase-Based 3D Near-Field Source Localization Techniques for UHF RFID,” Sensors, vol. 16, no. 7, p. 978, Jun. 2016, doi: 10.3390/s16070978.
X. Teng, Z. Shen, L. Huang, H. Li, and W. Li, “Multi-sensor fusion based wheeled robot research on indoor positioning method,” Results Eng., vol. 22, p. 102268, Jun. 2024, doi: 10.1016/j.rineng.2024.102268.
J. Wang and Y. Takahashi, “SLAM Method Based on Independent Particle Filters for Landmark Mapping and Localization for Mobile Robot Based on HF-band RFID System,” J. Intell. Robot. Syst., vol. 92, no. 3–4, pp. 413–433, Dec. 2018, doi: 10.1007/s10846-017-0701-8.
M. Hasani, J. Talvitie, L. Sydanheimo, E.-S. Lohan, and L. Ukkonen, “Hybrid WLAN-RFID Indoor Localization Solution Utilizing Textile Tag,” IEEE Antennas Wirel. Propag. Lett., vol. 14, pp. 1358–1361, 2015, doi: 10.1109/LAWP.2015.2406951.
C. Wu, Z. Gong, B. Tao, K. Tan, Z. Gu, and Z.-P. Yin, “RF-SLAM: UHF-RFID Based Simultaneous Tags Mapping and Robot Localization Algorithm for Smart Warehouse Position Service,” IEEE Trans. Ind. Informatics, vol. 19, no. 12, pp. 11765–11775, Dec. 2023, doi: 10.1109/TII.2023.3252405.
K. Srinivasan and J. Gu, “Multiple Sensor Fusion in Mobile Robot Localization,” in 2007 Canadian Conference on Electrical and Computer Engineering, 2007, pp. 1207–1210. doi: 10.1109/CCECE.2007.308.
B. Tao, H. Wu, Z. Gong, Z. Yin, and H. Ding, “An RFID-Based Mobile Robot Localization Method Combining Phase Difference and Readability,” IEEE Trans. Autom. Sci. Eng., vol. 18, no. 3, pp. 1406–1416, Jul. 2021, doi: 10.1109/TASE.2020.3006724.
M. T. Nguyen and K. A. Teague, “Compressive and cooperative sensing in distributed mobile sensor networks,” in MILCOM 2015 - 2015 IEEE Military Communications Conference, Oct. 2015, pp. 1033–1038. doi: 10.1109/MILCOM.2015.7357581.
M. T. Nguyen and K. A. Teague, “Compressive sensing based random walk routing in wireless sensor networks,” Ad Hoc Networks, vol. 54, pp. 99–110, Jan. 2017, doi: 10.1016/j.adhoc.2016.10.009.
S. Ahuja and P. Potti, “An Introduction to RFID Technology,” Commun. Netw., vol. 02, no. 03, pp. 183–186, 2010, doi: 10.4236/cn.2010.23026.
M. T. Nguyen, “Data collection algorithms in wireless sensor networks employing compressive sensing,” Oklahoma State University, 2015.
C. Li, L. Mo, and D. Zhang, “Review on UHF RFID Localization Methods,” IEEE J. Radio Freq. Identif., vol. 3, no. 4, pp. 205–215, Dec. 2019, doi: 10.1109/JRFID.2019.2924346.
A. Motroni, A. Buffi, and P. Nepa, “A Survey on Indoor Vehicle Localization Through RFID Technology,” IEEE Access, vol. 9, pp. 17921–17942, 2021, doi: 10.1109/ACCESS.2021.3052316.
C. Chen, Y. Wang, Y. Zhang, and Y. Zhai, “Indoor Positioning Algorithm Based on Nonlinear PLS Integrated With RVM,” IEEE Sens. J., vol. 18, no. 2, pp. 660–668, Jan. 2018, doi: 10.1109/JSEN.2017.2772798.
A. Motroni, F. Bernardini, A. Buffi, P. Nepa, and B. Tellini, “A UHF-RFID Multi-Antenna Sensor Fusion Enables Item and Robot Localization,” IEEE J. Radio Freq. Identif., vol. 6, pp. 456–466, 2022, doi: 10.1109/JRFID.2022.3166354.
A. M. Whitney, J. M. Parker, Z. C. N. Kratzer, J. T. Fessler, and J. G. Whitney, “Reducing RF Distance Error by Characterizing Multipath,” IEEE Trans. Instrum. Meas., vol. 68, no. 9, pp. 3329–3338, Sep. 2019, doi: 10.1109/TIM.2018.2875899.
H. Xu, Y. Ding, P. Li, R. Wang, and Y. Li, “An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor,” Sensors, vol. 17, no. 8, p. 1806, Aug. 2017, doi: 10.3390/s17081806.
X. Guo, N. R. Elikplim, N. Ansari, L. Li, and L. Wang, “Robust WiFi Localization by Fusing Derivative Fingerprints of RSS and Multiple Classifiers,” IEEE Trans. Ind. Informatics, vol. 16, no. 5, pp. 3177–3186, May 2020, doi: 10.1109/TII.2019.2910664.
L. Li, X. Guo, N. Ansari, and H. Li, “A Hybrid Fingerprint Quality Evaluation Model for WiFi Localization,” IEEE Internet Things J., vol. 6, no. 6, pp. 9829–9840, Dec. 2019, doi: 10.1109/JIOT.2019.2932464.
F. Martinelli, “A Robot Localization System Combining RSSI and Phase Shift in UHF-RFID Signals,” IEEE Trans. Control Syst. Technol., vol. 23, no. 5, pp. 1782–1796, Sep. 2015, doi: 10.1109/TCST.2014.2386777.
M. D. Nguyen and T. T. Nguyen, “Solutions to Improve Indoor Positioning Accuracy supporting Autonomous Mobile Robots,” J. Futur. Artif. Intell. Technol., vol. 2, no. 1, pp. 63–78, Apr. 2025, doi: 10.62411/faith.3048-3719-86.
J. Zhang et al., “Toward Robust RFID Localization via Mobile Robot,” IEEE/ACM Trans. Netw., vol. 32, no. 4, pp. 2904–2919, Aug. 2024, doi: 10.1109/TNET.2024.3373770.
D. Xie, X. Wang, A. Tang, and H. Zhu, “A Portable RFID Localization Approach for Mobile Robots,” IEEE Internet Things J., vol. 9, no. 23, pp. 23332–23347, Dec. 2022, doi: 10.1109/JIOT.2022.3202136.
H. Belaidi and F. Demim, “NURBs Based Multi-robots Path Planning with Obstacle Avoidance,” J. Comput. Theor. Appl., vol. 1, no. 4, pp. 478–491, May 2024, doi: 10.62411/jcta.10387.
H. T. Tran et al., “Extended Kalman Filter (EKF) Based Localization Algorithms for Mobile Robots Utilizing Vision and Odometry,” in 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), Jun. 2022, pp. 91–96. doi: 10.1109/MELECON53508.2022.9843066.
M. T. Nguyen, H. M. La, and K. A. Teague, “Collaborative and Compressed Mobile Sensing for Data Collection in Distributed Robotic Networks,” IEEE Trans. Control Netw. Syst., vol. 5, no. 4, pp. 1729–1740, Dec. 2018, doi: 10.1109/TCNS.2017.2754364.
S. Su et al., “Positioning Accuracy Improvement of Automated Guided Vehicles Based on a Novel Magnetic Tracking Approach,” IEEE Intell. Transp. Syst. Mag., vol. 12, no. 4, pp. 138–148, 2020, doi: 10.1109/MITS.2018.2880269.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Mui D. Nguyen, Thang C. Vu, Vu T. Hoang, Dung T. Nguyen, Tao V. Nguyen, Long Q. Dinh, Son Q. Tran, Vinh Q. Tran, Minh Nguyen
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
