Predictive Indoor Navigation using Commercial Smart-phones

Abstract

Low-cost navigation solutions for indoor environments have a variety of real-world applications ranging from emergency evacuation to mobility aids for people with disabilities. Challenges for indoor navigation include robust localization in the absence of GPS, intuitive recognition of user navigation goals, and efficient route-planning and re-planning techniques. In this paper, we present an architecture for indoor navigation that integrates observed behavior for recognizing user navigation goals and estimating future paths without direct input from the user. Our architecture comprises of three core components: effective localization, map representation and route planning, and plan recognition. The outlined architecture is unique in its integration of the core navigation and prediction components. To evaluate the feasibility of the proposed architecture, we develop a prototype application on a commercial smart-phone. The developed application was tested in an indoor environment and was found to accurately predict intended destination and to provide effective navigation guidance to the user.