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  • Deep learning based wireless localization for indoor navigation
  • MobiCom '20: Proceedings of the 26th Annual International Conference on Mobile Computing and Networking 논문

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 MobiCom '20: Proceedings of the 26th Annual International Conference on Mobile Computing and Networking / April 2020


『Deep learning based wireless localization for indoor navigation』


Authors

Roshan Ayyalasomayajula, Aditya Arun, Chenfeng Wu, Sanatan Sharma, Abhishek Rajkumar Sethi, Deepak Vasisht, Dinesh Bharadia


Abstract

Location services, fundamentally, rely on two components: a mapping system and a positioning system. The mapping system provides the physical map of the space, and the positioning system identifies the position within the map. Outdoor location services have thrived over the last couple of decades because of well-established platforms for both these components (e.g. Google Maps for mapping, and GPS for positioning). In contrast, indoor location services haven't caught up because of the lack of reliable mapping and positioning frameworks. Wi-Fi positioning lacks maps and is also prone to environmental errors. In this paper, we present DLoc, a Deep Learning based wireless localization algorithm that can overcome traditional limitations of RF-based localization approaches (like multipath, occlusions, etc.). We augment DLoc with an automated mapping platform, MapFind. MapFind constructs location-tagged maps of the environment and generates training data for DLoc. Together, they allow off-the-shelf Wi-Fi devices like smartphones to access a map of the environment and to estimate their position with respect to that map. During our evaluation, MapFind has collected location estimates of over 105 thousand points under 8 different scenarios with varying furniture positions and people motion across two different spaces covering 2000 sq. Ft. DLoc outperforms state-of-the-art methods in Wi-Fi-based localization by 80% (median & 90th percentile) across the two different spaces.


Review

Wifi를 이용한 실내 위치 파악 기술의 정확도를 상승시키기 위해 Lidar, 카메라, 주행 거리 측정기가 장착되어 있고 wifi 채널 상태 정보를 제공하는 MapFind로봇에서 얻은 데이터와 지도를 기반으로 이미지 변환 인공신경망인 DLoc을 학습시켜 가구의 위치가 변해도 스마트폰을 이용해 정확한 실내 위치를 파악할 수 있도록 하는 연구 


References 

1. Fadel Adib, Zach Kabelac, Dina Katabi, and Robert C. Miller. 2014. 3D Tracking via Body Radio Reflections (NSDI).

2. Fadel Adib and Dina Katabi. 2013. See through walls with WiFi! Vol. 43. ACM