TY - GEN
T1 - Predictive Indoor Navigation using Commercial Smart-phones
AU - Kannan, Balajee
AU - Meneguzzi, Felipe
AU - Dias, M. Bernanrdine
AU - Sycara, Katia
N1 - This work was partially sponsored by the Google Core AI gift
from 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.
PY - 2013
Y1 - 2013
N2 - 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, andeffective 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
AB - 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, andeffective 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
U2 - 10.1145/2480362.2480463
DO - 10.1145/2480362.2480463
M3 - Published conference contribution
SP - 519
EP - 525
BT - Proceedings of the 2013 ACM symposium on Applied computing
ER -
