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 language | English (US) |
|---|---|
| Title of host publication | Artificial Intelligence for Cities - Papers Presented at the 29th AAAI Conference on Artificial Intelligence, Technical Report |
| Publisher | AI Access Foundation |
| Pages | 28-34 |
| Number of pages | 7 |
| Volume | WS-15-04 |
| ISBN (Electronic) | 9781577357155 |
| State | Published - 2015 |
| Event | 29th AAAI Conference on Artificial Intelligence, AAAI 2015 - Austin, United States Duration: Jan 25 2015 → Jan 30 2015 |
Other
| Other | 29th AAAI Conference on Artificial Intelligence, AAAI 2015 |
|---|---|
| Country | United States |
| City | Austin |
| Period | 1/25/15 → 1/30/15 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Indoor trajectory identification : Snapping with uncertainty. / Shroff, Ravi; Zha, Yilong; Wang, Richard; Veloso, Manuela; Seshan, Srinivasan.
Artificial Intelligence for Cities - Papers Presented at the 29th AAAI Conference on Artificial Intelligence, Technical Report. Vol. WS-15-04 AI Access Foundation, 2015. p. 28-34.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Indoor trajectory identification
T2 - Snapping with uncertainty
AU - Shroff, Ravi
AU - Zha, Yilong
AU - Wang, Richard
AU - Veloso, Manuela
AU - Seshan, Srinivasan
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84964577610&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964577610&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84964577610
VL - WS-15-04
SP - 28
EP - 34
BT - Artificial Intelligence for Cities - Papers Presented at the 29th AAAI Conference on Artificial Intelligence, Technical Report
PB - AI Access Foundation
ER -