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Sensors 2015, 15(12), 30199-30220; doi:10.3390/s151229795

Passive Sensor Integration for Vehicle Self-Localization in Urban Traffic Environment

Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
This paper is an extended version of the paper entitled “GNSS/INS/on-board camera integration for vehicle self-localization in urban canyon”, presented at IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain, 15–18 September 2015.
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Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 12 October 2015 / Revised: 25 November 2015 / Accepted: 26 November 2015 / Published: 3 December 2015
(This article belongs to the Special Issue Sensors in New Road Vehicles)

Abstract

This research proposes an accurate vehicular positioning system which can achieve lane-level performance in urban canyons. Multiple passive sensors, which include Global Navigation Satellite System (GNSS) receivers, onboard cameras and inertial sensors, are integrated in the proposed system. As the main source for the localization, the GNSS technique suffers from Non-Line-Of-Sight (NLOS) propagation and multipath effects in urban canyons. This paper proposes to employ a novel GNSS positioning technique in the integration. The employed GNSS technique reduces the multipath and NLOS effects by using the 3D building map. In addition, the inertial sensor can describe the vehicle motion, but has a drift problem as time increases. This paper develops vision-based lane detection, which is firstly used for controlling the drift of the inertial sensor. Moreover, the lane keeping and changing behaviors are extracted from the lane detection function, and further reduce the lateral positioning error in the proposed localization system. We evaluate the integrated localization system in the challenging city urban scenario. The experiments demonstrate the proposed method has sub-meter accuracy with respect to mean positioning error. View Full-Text
Keywords: vehicle self-localization; sensor integration; 3D map; GNSS; inertial sensor; vision; lane detection; particle filter vehicle self-localization; sensor integration; 3D map; GNSS; inertial sensor; vision; lane detection; particle filter
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Gu, Y.; Hsu, L.-T.; Kamijo, S. Passive Sensor Integration for Vehicle Self-Localization in Urban Traffic Environment. Sensors 2015, 15, 30199-30220.

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