CARLOC: Precisely tracking automobile position

Yurong Jiang, Hang Qiu, Matthew McCartney, Gaurav Sukhatme, Marco Gruteser, Fan Bai, Donald Grimm, Ramesh Govindan

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

10 Citations (Scopus)

Abstract

Precise positioning of an automobile to within lane-level precision can enable better navigation and context-awareness. However, GPS by itself cannot provide such precision in obstructed urban environments. In this paper, we present a system called CARLOC for lanelevel positioning of automobiles. CARLOC uses three key ideas in concert to improve positioning accuracy: it uses digital maps to match the vehicle to known road segments; it uses vehicular sensors to obtain odometry and bearing information; and it uses crowd-sourced location estimates of roadway landmarks that can be detected by sensors available in modern vehicles. CARLOC unifies these ideas in a probabilistic position estimation framework, widely used in robotics, called the sequential Monte Carlo method. Through extensive experiments on a real vehicle, we show that CARLOC achieves sub-meter positioning accuracy in an obstructed urban setting, an order-of-magnitude improvement over a high-end GPS device.

Original languageEnglish (US)
Title of host publicationSenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems
PublisherAssociation for Computing Machinery, Inc
Pages253-265
Number of pages13
ISBN (Electronic)9781450336314
DOIs
StatePublished - Nov 1 2015
Event13th ACM Conference on Embedded Networked Sensor Systems, SenSys 2015 - Seoul, Korea, Republic of
Duration: Nov 1 2015Nov 4 2015

Publication series

NameSenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

Other

Other13th ACM Conference on Embedded Networked Sensor Systems, SenSys 2015
CountryKorea, Republic of
CitySeoul
Period11/1/1511/4/15

Fingerprint

Automobiles
Global positioning system
Bearings (structural)
Sensors
Navigation
Robotics
Monte Carlo methods
Experiments

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • Accuracy
  • GPS
  • Map

Cite this

Jiang, Y., Qiu, H., McCartney, M., Sukhatme, G., Gruteser, M., Bai, F., ... Govindan, R. (2015). CARLOC: Precisely tracking automobile position. In SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems (pp. 253-265). (SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems). Association for Computing Machinery, Inc. https://doi.org/10.1145/2809695.2809725
Jiang, Yurong ; Qiu, Hang ; McCartney, Matthew ; Sukhatme, Gaurav ; Gruteser, Marco ; Bai, Fan ; Grimm, Donald ; Govindan, Ramesh. / CARLOC : Precisely tracking automobile position. SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. Association for Computing Machinery, Inc, 2015. pp. 253-265 (SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems).
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abstract = "Precise positioning of an automobile to within lane-level precision can enable better navigation and context-awareness. However, GPS by itself cannot provide such precision in obstructed urban environments. In this paper, we present a system called CARLOC for lanelevel positioning of automobiles. CARLOC uses three key ideas in concert to improve positioning accuracy: it uses digital maps to match the vehicle to known road segments; it uses vehicular sensors to obtain odometry and bearing information; and it uses crowd-sourced location estimates of roadway landmarks that can be detected by sensors available in modern vehicles. CARLOC unifies these ideas in a probabilistic position estimation framework, widely used in robotics, called the sequential Monte Carlo method. Through extensive experiments on a real vehicle, we show that CARLOC achieves sub-meter positioning accuracy in an obstructed urban setting, an order-of-magnitude improvement over a high-end GPS device.",
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Jiang, Y, Qiu, H, McCartney, M, Sukhatme, G, Gruteser, M, Bai, F, Grimm, D & Govindan, R 2015, CARLOC: Precisely tracking automobile position. in SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, Association for Computing Machinery, Inc, pp. 253-265, 13th ACM Conference on Embedded Networked Sensor Systems, SenSys 2015, Seoul, Korea, Republic of, 11/1/15. https://doi.org/10.1145/2809695.2809725

CARLOC : Precisely tracking automobile position. / Jiang, Yurong; Qiu, Hang; McCartney, Matthew; Sukhatme, Gaurav; Gruteser, Marco; Bai, Fan; Grimm, Donald; Govindan, Ramesh.

SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. Association for Computing Machinery, Inc, 2015. p. 253-265 (SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems).

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

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AB - Precise positioning of an automobile to within lane-level precision can enable better navigation and context-awareness. However, GPS by itself cannot provide such precision in obstructed urban environments. In this paper, we present a system called CARLOC for lanelevel positioning of automobiles. CARLOC uses three key ideas in concert to improve positioning accuracy: it uses digital maps to match the vehicle to known road segments; it uses vehicular sensors to obtain odometry and bearing information; and it uses crowd-sourced location estimates of roadway landmarks that can be detected by sensors available in modern vehicles. CARLOC unifies these ideas in a probabilistic position estimation framework, widely used in robotics, called the sequential Monte Carlo method. Through extensive experiments on a real vehicle, we show that CARLOC achieves sub-meter positioning accuracy in an obstructed urban setting, an order-of-magnitude improvement over a high-end GPS device.

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Jiang Y, Qiu H, McCartney M, Sukhatme G, Gruteser M, Bai F et al. CARLOC: Precisely tracking automobile position. In SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. Association for Computing Machinery, Inc. 2015. p. 253-265. (SenSys 2015 - Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems). https://doi.org/10.1145/2809695.2809725