  Indoor Positioning Systems (IPS) traditionally rely on odometry and building
infrastructures like WiFi, often supplemented by building floor plans for
increased accuracy. However, the limitation of floor plans in terms of
availability and timeliness of updates challenges their wide applicability. In
contrast, the proliferation of smartphones and WiFi-enabled robots has made
crowdsourced radio maps - databases pairing locations with their corresponding
Received Signal Strengths (RSS) - increasingly accessible. These radio maps not
only provide WiFi fingerprint-location pairs but encode movement regularities
akin to the constraints imposed by floor plans. This work investigates the
possibility of leveraging these radio maps as a substitute for floor plans in
multimodal IPS. We introduce a new framework to address the challenges of radio
map inaccuracies and sparse coverage. Our proposed system integrates an
uncertainty-aware neural network model for WiFi localization and a bespoken
Bayesian fusion technique for optimal fusion. Extensive evaluations on multiple
real-world sites indicate a significant performance enhancement, with results
showing ~ 25% improvement over the best baseline
