Research on Fast Identification and Location of Contour Features of Electric Vehicle Charging Port in Complex Scenes

• Published in: IEEE access Vol. 10; pp. 26702 - 26714
• Main Authors: Quan, Pengkun, Lou, Ya'Nan, Lin, Haoyu, Liang, Zhuo, Di, Shichun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automatic charging; Calibration; Cameras; Contours; Distortion; Electric vehicle charging; electric vehicle charging port; Electric vehicles; Ellipses; monocular vision; non-cooperative characteristics; Ports (computers); pose estimation; Recognition; Robot vision systems; Robots
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3092210

The maturity of automatic driving and parking technologies is gradually driving electric vehicle charging toward automation. The primary condition of automatic charging that has a high significance is the identification of electric vehicle charging ports. This research proposes an automatic system for the identification and positioning of charging ports of electric vehicles. The system is mainly divided into rough and precise positioning. The former is based on the Hough circle and the Hough line, and locates the position information of the charging port. The latter uses the Canny operator to obtain the contour information of the original and gradient images respectively. All the contours of the two images are fitted into ellipses by the quadratic curve standardization (QCS) method, and irrelevant ellipses are screened out. Finally, the perspective-n-point (PNP) algorithm is used to locate the pose information of the charging port. The aubo-i10 6-DOF articulated robot is used to test the recognition and insertion accuracies in different environments. The results show that the average recognition rate of rough positioning is 97.9%, the average displacement error of precise positioning in X, Y and Z directions are 0.60, 0.83 and 1.23 mm, respectively, and the average angle errors in RX, RY and RZ directions are 1.19, 0.97 and 0.50 degrees, respectively. The average success rate is 94.8%. These results demonstrate that the proposed system meets the basic plug-in requirements of electric vehicle charging ports.