Indoor trajectory identification: Snapping with uncertainty
Richard Wang, Ravi Shroff, Yilong Zha, Srinivasan Seshan, Manuela Veloso
Abstract
We consider the problem of indoor human trajectory identification using odometry data from smartphone sensors. Given a segmented trajectory, a simplified map of the environment, and a set of error thresholds, we implement a map-matching algorithm in a urban setting and analyze the accuracy of the resulting path. We also discuss aggregation of user step data into a segmented trajectory. Besides providing an interesting application of learning human motion in a constrained environment, we examine how the uncertainty of the snapped trajectory varies with path length. We demonstrate that as new segments are added to a path, the number of possibilities for earlier segments is monotonically non-increasing. Applications of this work in an urban setting are discussed, as well as future plans to develop a formal theory of odometry-based map-matching.
BibTeX
@inproceedings{iros2015_indoortrajectory,
title = {Indoor trajectory identification: Snapping with uncertainty},
author = {Richard Wang and Ravi Shroff and Yilong Zha and Srinivasan Seshan and Manuela Veloso},
booktitle = {IROS 2015},
year = {2015}
}