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 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
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 language | English |
|---|---|
| Title of host publication | Proceedings of the 2013 ACM symposium on Applied computing |
| Pages | 519-525 |
| Number of pages | 7 |
| DOIs | |
| Publication status | Published - 2013 |
| Externally published | Yes |
Bibliographical note
This work was partially sponsored by the Google Core AI giftfrom Google Inc. This paper does not necessarily reflect the opin-
ion or policy of the sponsors; no official endorsement should be
inferred. The authors also thank members of the rCommerce Lab-
oratory at Carnegie Mellon University for their valuable contri-
bution during development and testing, as well as the RI Summer
Scholars program for making the author’s collaboration possible.
