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We present a novel egocentric visual localization algorithm for an indoor navigation system, called PERCEPT-V, which is designed to assist the blind and visually impaired users traveling independently in an unfamiliar indoor space. Through the integration of a background extraction module based on Robust Principle Component Analysis (RPCA) into the localization algorithm, we successfully improve the resilience of camera localization to the presence of crowds in the observed scene. Experiments using datasets of videos containing various levels of crowd activity show that the proposed algorithm can increase prominently the reliability of localization performance. 

We introduce PERCEPT system, an indoor navigation system for the blind and visually impaired. PERCEPT will improve the quality of life and health of the visually impaired community by enabling independent living. Using PERCEPT, blind users will have independent access to public health facilities such as clinics, hospitals, and wellness centers. Access to healthcare facilities is crucial for this population due to the multiple health conditions that they face such as diabetes and its complications. PERCEPT system trials with 24 blind and visually impaired users in a multistory building show PERCEPT system effectiveness in providing appropriate navigation instructions to these users. The uniqueness of our system is that it is affordable and that its design follows orientation and mobility principles. We hope that PERCEPT will become a standard deployed in all indoor public spaces, especially in healthcare and wellness facilities.