Indoor trajectory identification: Snapping with uncertainty

Ravi Shroff, Yilong Zha, Richard Wang, Manuela Veloso, Srinivasan Seshan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider the problem of indoor human trajectory identification using odomctry 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.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence for Cities - Papers Presented at the 29th AAAI Conference on Artificial Intelligence, Technical Report
PublisherAI Access Foundation
Pages28-34
Number of pages7
ISBN (Electronic)9781577357155
StatePublished - 2015
Event29th AAAI Conference on Artificial Intelligence, AAAI 2015 - Austin, United States
Duration: Jan 25 2015Jan 30 2015

Publication series

NameAAAI Workshop - Technical Report
VolumeWS-15-04

Other

Other29th AAAI Conference on Artificial Intelligence, AAAI 2015
CountryUnited States
CityAustin
Period1/25/151/30/15

ASJC Scopus subject areas

  • Engineering(all)

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