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28 September 2022 Research on multi-sensor assisted WiFi signal fingerprint indoor location method based on extended Kalman filter
Weiping Guo, Tongyue Gao, Daizhuang Bai, Jinwang Li, Xiaobing Wang
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Proceedings Volume 12339, Second International Conference on Cloud Computing and Mechatronic Engineering (I3CME 2022); 1233910 (2022) https://doi.org/10.1117/12.2652473
Event: Second International Conference on Cloud Computing and Mechatronic Engineering (I3CME 2022), 2022, Chendu, China
Abstract
In the field of the mobile robot indoor location, aiming at the problems of poor stability of the WiFi signal fingerprint location and low accuracy of the single sensor location, this paper proposes a multi-sensor fusion assisted WiFi signal fingerprint location method for a mobile robot. This method is based on the extended Kalman filter (EKF) algorithm, combined with the trajectory information obtained from the inertial measurement unit (IMU) and the odometer, to fuse and correct the WiFi signal fingerprint positioning results, so as to realize a fusion positioning method with WiFi positioning as the main and multi-sensor positioning as the auxiliary. The experimental results show that the average positioning error of the fusion positioning algorithm proposed in this paper is controlled at 0.98 m, which can effectively solve the problem that fingerprint positioning using WiFi signal is greatly disturbed by the environment, and avoid the cumulative error caused by dead reckoning (DR), and improve the robustness and positioning accuracy of the positioning system.
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Weiping Guo, Tongyue Gao, Daizhuang Bai, Jinwang Li, and Xiaobing Wang "Research on multi-sensor assisted WiFi signal fingerprint indoor location method based on extended Kalman filter", Proc. SPIE 12339, Second International Conference on Cloud Computing and Mechatronic Engineering (I3CME 2022), 1233910 (28 September 2022); https://doi.org/10.1117/12.2652473
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KEYWORDS
Mobile robots
Received signal strength
Filtering (signal processing)
Detection and tracking algorithms
Sensors
Data modeling
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