Traditional Indoor Positioning Systems (IPS) use odometry, WiFi, and often building floor plans for accuracy. However, floor plan limitations have shifted attention to crowdsourced 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.
