TY - GEN
T1 - Vehicle Positioning Based on Road-side Features and Matching Techniques
AU - Wang, Sheng Lun
AU - Juang, Jyh Ching
AU - Tsai, Hung Yih
N1 - Funding Information:
This work is financially supported in part by the Automotive Research & Testing Center (ARTC), Taiwan, under Contract BB-07-0069 and by the Ministry of Science and Technology (MOST), Taiwan, under grant MOST-105-2221-E-006 -106 -MY3.
Funding Information:
ACKNOWLEDGMENT This work is financially supported in part by the Automotive Research & Testing Center (ARTC), Taiwan, under Contract BB-07-0069 and by the Ministry of Science and Technology (MOST), Taiwan, under grant MOST-105-2221-E-006-106-MY3.
Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/9
Y1 - 2019/1/9
N2 - The accuracy and reliability of the vehicle positioning system are important performance indices of advanced driver assisted systems and the autonomous driving systems. The paper focuses on the development and verification of vehicle positioning techniques by using Lidar and GPS/IMU sensors. To this end, the techniques for feature extraction, map building, and point cloud matching are investigated. The techniques are then integrated and implemented in a robotic operating system (ROS) platform. Experimental results verify the feasibility of the proposed sensor fusion technique with roadside feature extraction characteristics in rendering high accuracy and reliability vehicle positioning.
AB - The accuracy and reliability of the vehicle positioning system are important performance indices of advanced driver assisted systems and the autonomous driving systems. The paper focuses on the development and verification of vehicle positioning techniques by using Lidar and GPS/IMU sensors. To this end, the techniques for feature extraction, map building, and point cloud matching are investigated. The techniques are then integrated and implemented in a robotic operating system (ROS) platform. Experimental results verify the feasibility of the proposed sensor fusion technique with roadside feature extraction characteristics in rendering high accuracy and reliability vehicle positioning.
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U2 - 10.1109/CACS.2018.8606772
DO - 10.1109/CACS.2018.8606772
M3 - Conference contribution
AN - SCOPUS:85062406036
T3 - 2018 International Automatic Control Conference, CACS 2018
BT - 2018 International Automatic Control Conference, CACS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Automatic Control Conference, CACS 2018
Y2 - 4 November 2018 through 7 November 2018
ER -