Mobile Panoramic Vision for Assisting the Blind via Indexing and Localization

In this paper, we propose a first-person localization and navigation system for helping blind and visually-impaired people navigate in indoor environments. The system consists of a mobile vision front end with a portable panoramic lens mounted on a smart phone, and a remote GPU-enabled server. Compact and effective omnidirectional video features are extracted and represented in the smart phone front end, and then transmitted to the server, where the features of an input image or a short video clip are used to search a database of an indoor environment via image-based indexing to find both the location and the orientation of the current view. To deal with the high computational cost in searching a large database for a realistic navigation application, data parallelism and task parallelism properties are identified in database indexing, and computation is accelerated by using multi-core CPUs and GPUs. Experiments on synthetic data and real data are carried out to demonstrate the capacity of the proposed system, with respect to real-time response and robustness.