AAAI Publications, Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence

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Indoor Trajectory Identification: Snapping with Uncertainty
Ravi Shroff, Yilong Zha, Richard Wang, Manuela Veloso, Srinivasan Seshan

Last modified: 2015-04-01


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 decreases monotonically. 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.


odometry; trajectory identification; indoor localization

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