Predictive Indoor Navigation using Commercial Smart-phones

Balajee Kannan, Felipe Meneguzzi, M. Bernanrdine Dias, Katia Sycara

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

15 Citations (Scopus)


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 commercial indoor navigation solutions include robust localization, intuitive recognition of user navigation goals, and efficient route-planning and re-planning techniques for resource-constrained platforms like smart-phones and mobile phones. In this paper, we present an architecture for indoor navigation using an Android smartphone that integrates observed behavior for recognizing user navigation goals and estimating future paths without direct input from the user. Our architecture contains three core components: plan recognition, map representation and route planning, and
effective localization. To evaluate the feasibility of our solution, we develop a prototype application on a commercial smart-phone and tested it in multiple indoor environments
Original languageEnglish
Title of host publicationProceedings of the 2013 ACM symposium on Applied computing
Number of pages7
Publication statusPublished - 2013


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