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

本研究论文探讨了利用众包无线电地图作为多模式室内定位系统中楼层平面图的替代方案，通过引入一种新的框架来解决无线电地图不准确和覆盖不足的挑战，并结合一种基于深度学习的 WiFi 定位模型和一种贝叶斯融合技术来实现最佳融合，通过在多个真实场景上的广泛评估，结果显示相比最佳基线系统有约 25% 的性能提升。