Indoor localization is of great importance to a wide range of applications in this era of mobile computing, attracting extensive research effort over recent decades. Current mainstream solutions rely on Received Signal Strength (RSS) of wireless signals as fingerprints to distinguish and infer locations. However, those methods suffer from fingerprint ambiguity that roots in multipath fading and temporal dynamics of wireless signals, which invalidate theoretical propagation models, distort received signal signatures, and fundamentally constrain the performance of indoor localization. With the trend moving towards equipment of smart devices in daily life and adoption of enhanced sensors, we identify the opportunity of ubiquitous indoor localization service via the multi-modal sens...[ Read more ]

Indoor localization is of great importance to a wide range of applications in this era of mobile computing, attracting extensive research effort over recent decades. Current mainstream solutions rely on Received Signal Strength (RSS) of wireless signals as fingerprints to distinguish and infer locations. However, those methods suffer from fingerprint ambiguity that roots in multipath fading and temporal dynamics of wireless signals, which invalidate theoretical propagation models, distort received signal signatures, and fundamentally constrain the performance of indoor localization. With the trend moving towards equipment of smart devices in daily life and adoption of enhanced sensors, we identify the opportunity of ubiquitous indoor localization service via the multi-modal sensing abilities on smartphones. Firstly, we propose Argus, an image-assisted localization solution for mobile devices by harnessing their Visual Sensing abilities. The basic idea of Argus is to extract geometric constraints from crowdsourced photos, and to reduce fingerprint ambiguity by mapping the constraints jointly against the fingerprint space. Secondly, we design TUM, an Acoustic Sensing localization scheme Towards Ubiquitous Multi-device localization. The basic idea of RAD is to utilize the dual-microphones and speakers to obtain distance cues among devices, while resolving the localization ambiguity with the help of MEMS sensors. Thirdly, we exploit the Inertial Sensing abilities on smartphones and propose RAD. The basic idea is to automatically generate a fingerprint database through space partition, while achieving fine-grained localization via a discretized particle filter with sensor data fusion. Finally, we design an indoor localization system ClickLoc that achieves sub-meter accuracy by harnessing Multi-Modal Sensing abilities on smartphones. We prototype the above schemes with commodity devices, and evaluate their performances in various indoor environments. Experimental results demonstrate improved indoor localization accuracy, better user interaction and less overhead compared with classical RSS-based schemes.