Curb feature based localization of a mobile robot in urban road environments

Hyunsuk Lee; Jooyoung Park; Woo Jin Chung

doi:10.1109/ICRA.2015.7139579

Curb feature based localization of a mobile robot in urban road environments

Hyunsuk Lee, Jooyoung Park, Woo Jin Chung

School of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

Urban road environments that have pavement and curb are characterized as semi-structured road environments. In semi-structured road environments, the curb provides useful information for robot navigation. In this paper, we present a practical localization method for outdoor mobile robots using the curb features in semi-structured road environments. The curb features are especially useful in urban environment, where the GPS failures take place frequently. A curb extraction is conducted on the basis of the Kernel Fisher Discriminant Analysis (KFDA) to minimize false detection. We adopt the Extended Kalman Filter (EKF) to combine the curb information with odometry and Differential Global Positioning System (DGPS). The uncertainty models for the sensors are quantitatively analyzed to provide a practical solution.

Original language	English
Title of host publication	Proceedings - IEEE International Conference on Robotics and Automation
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2794-2799
Number of pages	6
Volume	2015-June
Edition	June
DOIs	https://doi.org/10.1109/ICRA.2015.7139579
Publication status	Published - 2015 Jun 29
Event	2015 IEEE International Conference on Robotics and Automation, ICRA 2015 - Seattle, United States Duration: 2015 May 26 → 2015 May 30

Other

Other	2015 IEEE International Conference on Robotics and Automation, ICRA 2015
Country/Territory	United States
City	Seattle
Period	15/5/26 → 15/5/30

ASJC Scopus subject areas

Software
Artificial Intelligence
Control and Systems Engineering
Electrical and Electronic Engineering

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10.1109/ICRA.2015.7139579

Cite this

Curb feature based localization of a mobile robot in urban road environments. / Lee, Hyunsuk; Park, Jooyoung; Chung, Woo Jin.

Proceedings - IEEE International Conference on Robotics and Automation. Vol. 2015-June June. ed. Institute of Electrical and Electronics Engineers Inc., 2015. p. 2794-2799 7139579.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, H, Park, J & Chung, WJ 2015, Curb feature based localization of a mobile robot in urban road environments. in Proceedings - IEEE International Conference on Robotics and Automation. June edn, vol. 2015-June, 7139579, Institute of Electrical and Electronics Engineers Inc., pp. 2794-2799, 2015 IEEE International Conference on Robotics and Automation, ICRA 2015, Seattle, United States, 15/5/26. https://doi.org/10.1109/ICRA.2015.7139579

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title = "Curb feature based localization of a mobile robot in urban road environments",

abstract = "Urban road environments that have pavement and curb are characterized as semi-structured road environments. In semi-structured road environments, the curb provides useful information for robot navigation. In this paper, we present a practical localization method for outdoor mobile robots using the curb features in semi-structured road environments. The curb features are especially useful in urban environment, where the GPS failures take place frequently. A curb extraction is conducted on the basis of the Kernel Fisher Discriminant Analysis (KFDA) to minimize false detection. We adopt the Extended Kalman Filter (EKF) to combine the curb information with odometry and Differential Global Positioning System (DGPS). The uncertainty models for the sensors are quantitatively analyzed to provide a practical solution.",

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booktitle = "Proceedings - IEEE International Conference on Robotics and Automation",

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N2 - Urban road environments that have pavement and curb are characterized as semi-structured road environments. In semi-structured road environments, the curb provides useful information for robot navigation. In this paper, we present a practical localization method for outdoor mobile robots using the curb features in semi-structured road environments. The curb features are especially useful in urban environment, where the GPS failures take place frequently. A curb extraction is conducted on the basis of the Kernel Fisher Discriminant Analysis (KFDA) to minimize false detection. We adopt the Extended Kalman Filter (EKF) to combine the curb information with odometry and Differential Global Positioning System (DGPS). The uncertainty models for the sensors are quantitatively analyzed to provide a practical solution.

AB - Urban road environments that have pavement and curb are characterized as semi-structured road environments. In semi-structured road environments, the curb provides useful information for robot navigation. In this paper, we present a practical localization method for outdoor mobile robots using the curb features in semi-structured road environments. The curb features are especially useful in urban environment, where the GPS failures take place frequently. A curb extraction is conducted on the basis of the Kernel Fisher Discriminant Analysis (KFDA) to minimize false detection. We adopt the Extended Kalman Filter (EKF) to combine the curb information with odometry and Differential Global Positioning System (DGPS). The uncertainty models for the sensors are quantitatively analyzed to provide a practical solution.

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Curb feature based localization of a mobile robot in urban road environments

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