ONavi: Data-driven based Multi-sensor Fusion Positioning System in Indoor Environments

This paper proposes a multi-sensor fusion system, named ONavi, that fuses WiFi and IMIJ to provide an accurate positioning service on smartphones in indoor environments. In this system, a hybrid CNN-Transformer-based neural network is proposed for our Pedestrian Dead Reckoning(PDR), which outperforms existing state-of-the-art methods. Additionally, in our data-driven WiFi positioning module, instead of pure RSSI based WiFi feature, a "Fusion BSSID-RSSI" feature is proposed, which significantly improves positioning accuracy. Eventually, we use a loosely coupled optimization-based framework to fuse the aforementioned positioning results. Quantitative evaluations demonstrate that ONavi is capable of achieving outstanding performance of positioning estimation in real indoor environment.