Indoor trajectory identification

Snapping with uncertainty

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

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

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.

Original languageEnglish (US)
Title of host publicationIROS Hamburg 2015 - Conference Digest
Subtitle of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4901-4906
Number of pages6
Volume2015-December
ISBN (Electronic)9781479999941
DOIs
StatePublished - Dec 11 2015
EventIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015 - Hamburg, Germany
Duration: Sep 28 2015Oct 2 2015

Other

OtherIEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015
CountryGermany
CityHamburg
Period9/28/1510/2/15

Fingerprint

Trajectories
Smartphones
Agglomeration
Uncertainty
Sensors

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this

Wang, R., Shroff, R., Zha, Y., Seshan, S., & Veloso, M. (2015). Indoor trajectory identification: Snapping with uncertainty. In IROS Hamburg 2015 - Conference Digest: IEEE/RSJ International Conference on Intelligent Robots and Systems (Vol. 2015-December, pp. 4901-4906). [7354066] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS.2015.7354066

Indoor trajectory identification : Snapping with uncertainty. / Wang, Richard; Shroff, Ravi; Zha, Yilong; Seshan, Srinivasan; Veloso, Manuela.

IROS Hamburg 2015 - Conference Digest: IEEE/RSJ International Conference on Intelligent Robots and Systems. Vol. 2015-December Institute of Electrical and Electronics Engineers Inc., 2015. p. 4901-4906 7354066.

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

Wang, R, Shroff, R, Zha, Y, Seshan, S & Veloso, M 2015, Indoor trajectory identification: Snapping with uncertainty. in IROS Hamburg 2015 - Conference Digest: IEEE/RSJ International Conference on Intelligent Robots and Systems. vol. 2015-December, 7354066, Institute of Electrical and Electronics Engineers Inc., pp. 4901-4906, IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015, Hamburg, Germany, 9/28/15. https://doi.org/10.1109/IROS.2015.7354066
Wang R, Shroff R, Zha Y, Seshan S, Veloso M. Indoor trajectory identification: Snapping with uncertainty. In IROS Hamburg 2015 - Conference Digest: IEEE/RSJ International Conference on Intelligent Robots and Systems. Vol. 2015-December. Institute of Electrical and Electronics Engineers Inc. 2015. p. 4901-4906. 7354066 https://doi.org/10.1109/IROS.2015.7354066
Wang, Richard ; Shroff, Ravi ; Zha, Yilong ; Seshan, Srinivasan ; Veloso, Manuela. / Indoor trajectory identification : Snapping with uncertainty. IROS Hamburg 2015 - Conference Digest: IEEE/RSJ International Conference on Intelligent Robots and Systems. Vol. 2015-December Institute of Electrical and Electronics Engineers Inc., 2015. pp. 4901-4906
@inproceedings{c143b4c08398429c909c812447c14832,
title = "Indoor trajectory identification: Snapping with uncertainty",
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.",
author = "Richard Wang and Ravi Shroff and Yilong Zha and Srinivasan Seshan and Manuela Veloso",
year = "2015",
month = "12",
day = "11",
doi = "10.1109/IROS.2015.7354066",
language = "English (US)",
volume = "2015-December",
pages = "4901--4906",
booktitle = "IROS Hamburg 2015 - Conference Digest",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - Indoor trajectory identification

T2 - Snapping with uncertainty

AU - Wang, Richard

AU - Shroff, Ravi

AU - Zha, Yilong

AU - Seshan, Srinivasan

AU - Veloso, Manuela

PY - 2015/12/11

Y1 - 2015/12/11

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84958149637&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84958149637&partnerID=8YFLogxK

U2 - 10.1109/IROS.2015.7354066

DO - 10.1109/IROS.2015.7354066

M3 - Conference contribution

VL - 2015-December

SP - 4901

EP - 4906

BT - IROS Hamburg 2015 - Conference Digest

PB - Institute of Electrical and Electronics Engineers Inc.

ER -