Pedestrian dead reckoning is a well-known approach for indoor navigation. There, the smartphone's inertial sensors readings are used to determine the user position by utilizing empirical or bio-mechanical approaches and by direct integration. In this paper, we propose PDRNet, a deep-learning pedestrian dead reckoning framework, for user positioning. It includes a smartphone location recognition classification network followed by a change of heading and distance regression network. Experimental results using a publicly available dataset show that the proposed approach outperforms traditional approaches and other deep learning based ones.
Bibliographical notePublisher Copyright:
© 2001-2012 IEEE.
- Pedestrian dead reckoning
- indoor navigation
- inertial sensors
- residual networks
- smartphone location recognition
ASJC Scopus subject areas
- Electrical and Electronic Engineering