Xiangyu Wen
ABSTRACT
Traditional Indoor Positioning Systems (IPS) use odometry, WiFi, and often building floor plans for accuracy. However, floor plan limitations have shifted attention to crowd-sourced radio maps, popularized by smartphones and WiFi-integrated robots. These maps pair locations with Received Signal Strengths (RSS) and reflect movement patterns similar to floor plans. Our research explores using radio maps as an alternative to floor plans in IPS. We've developed a new framework that combines an uncertainty-aware neural network for WiFi positioning with a Bayesian fusion method. Testing in real-world scenarios showed about a 25% performance increase compared to the leading baseline.
- Sachini Herath, Saghar Irandoust, Bowen Chen, Yiming Qian, Pyojin Kim, and Yasutaka Furukawa. 2021. Fusion-DHL: WiFi, IMU, and Floor-plan Fusion for Dense History of Locations in Indoor Environments. arXiv:2105.08837 [cs.RO]Google Scholar
- Rory Hughes, Lei Tao, Ilari Vallivaara, and Firas Alsehly. 2023. Calibration-free radiomap construction based on graph map matching.Google Scholar
- Silverman and Bernard W. 2018. Density estimation for statistics and data analysis. Routledge.Google Scholar
- Vaswani, Ashish, Noam Shazeer, Parmar, Niki, Jakob Uszkoreit, Jones, Llion, Aidan N Gomez, Kaiser, Łukasz, Polosukhin, and Illia. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).Google Scholar
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