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Paper ThCT3.4

Wang, Richard (Carnegie Mellon University), Shroff, Ravi (New York University), Zha, Yilong (New York University), Seshan, Srinivasan (Carnegie Mellon University), Veloso, Manuela (Carnegie Mellon University)

Indoor Trajectory Identification: Snapping with Uncertainty

Scheduled for presentation during the Regular session "Human Detection and Tracking" (ThCT3), Thursday, October 1, 2015, 12:05−12:20, Saal E

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sept 28 - Oct 03, 2015, Congress Center Hamburg, Hamburg, Germany

This information is tentative and subject to change. Compiled on July 20, 2019

Keywords Human Detection and Tracking, Localization

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.

 

 

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