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ThinkMind // ICAS 2020, The Sixteenth International Conference on Autonomic and Autonomous Systems // View article icas_2020_1_70_20052

Integration of Landmark Detection and Low-cost Sensors for Vehicle Localization in Challenging Environments

Authors:
Yu Hsiang Wang
Jyh Ching Juang
Muhammad Rony Hidayatullah

Keywords: autonomous vehicle; computer vision; vehicle localization; high definition map; sensor fusion

Abstract:
A seamless vehicle localization capability with high accuracy and integrity is essential for the safe operation of automated vehicles. This study integrates a map-matching based detection scheme and a low-cost Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) system to enhance the localization capability in a challenging environment. Existing vehicle navigation systems typically use a GNSS/IMU navigation suite to provide position, velocity, and attitude. Such a navigation suite is subject to the error characteristics of the IMU and the operating environment of the GNSS. If the GNSS signals are affected for a long period of time and the quality of the IMU is not well calibrated, erroneous navigation results may occur. It is noted that a challenging environment is featured with some landmarks such as traffic lights. The significant visual feature can be detected robustly by using a deep learning model in a whole day time, which means the availability of the proposed method is better than previous vision-based localization schemes. The paper investigates the fusion of a low-cost GNSS receiver, IMU, vehicle odometer, monocular camera, and an HD map to render seamless navigation. The system is implemented in a vehicle and tested at Taiwan CAR Lab. The effectiveness of the proposed scheme is demonstrated.

Pages: 45 to 52

Copyright: Copyright (c) IARIA, 2020

Publication date: September 27, 2020

Published in: conference

ISSN: 2308-3913

ISBN: 978-1-61208-787-0

Location: Lisbon, Portugal

Dates: from September 27, 2020 to October 1, 2020

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