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

翻译：室内定位系统（IPS）传统上依赖里程计和WiFi等建筑基础设施，通常辅以建筑平面图以提高精度。然而，平面图在可用性和更新时效性方面的局限性挑战了其广泛适用性。相比之下，智能手机和具备WiFi功能的设备的普及使得众包无线地图——将位置与相应接收信号强度（RSS）配对的数据集——日益可及。这些无线地图不仅提供WiFi指纹-位置对，还编码了类似于平面图约束的运动规律。本研究探讨了在多模态IPS中利用这些无线地图替代平面图的可能性。我们提出了一种新框架以应对无线地图不准确和覆盖稀疏的挑战。所提出的系统集成了用于WiFi定位的不确定性感知神经网络模型和用于最优融合的定制贝叶斯融合技术。在多个真实场景上的广泛评估表明，性能显著提升，结果显示相较于最佳基线方法改进约25%。