Research on Fast Recognition and Localization of an Electric Vehicle Charging Port Based on a Cluster Template Matching Algorithm

• Main Authors: Di, S. (Author), Liang, Z. (Author), Lin, H. (Author), Lou, Y. (Author), Quan, P. (Author), Wei, D. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Charging (batteries); cluster template matching algorithm; Cluster template matching algorithm; Cluster templates; Clustering algorithms; DC charging port for electric vehicle; DC charging port for electric vehicles; Electric vehicle charging; Electric vehicles; Matchings; Non-cooperative; Non-cooperative feature; non-cooperative features; pose estimation; Pose-estimation; Template matching; Template-matching algorithms; Textures; unmanned charging; Unmanned charging
• Online Access: View Fulltext in Publisher

 With the gradual maturity of driverless and automatic parking technologies, electric vehicle charging has been gradually developing in the direction of automation. However, the pose calculation of the charging port (CP) is an important part of realizing automatic charging, and it represents a problem that needs to be solved urgently. To address this problem, this paper proposes a set of efficient and accurate methods for determining the pose of an electric vehicle CP, which mainly includes the search and aiming phases. In the search phase, the feature circle algorithm is used to fit the ellipse information to obtain the pixel coordinates of the feature point. In the aiming phase, contour matching and logarithmic evaluation indicators are used in the cluster template matching algorithm (CTMA) proposed in this paper to obtain the matching position. Based on the image deformation rate and zoom rates, a matching template is established to realize the fast and accurate matching of textureless circular features and complex light fields. The EPnP algorithm is employed to obtain the pose information, and an AUBO-i5 robot is used to complete the charging gun insertion. The results show that the average CP positioning errors (x, y, z, Rx, Ry, and Rz) of the proposed algorithm are 0.65 mm, 0.84 mm, 1.24 mm, 1.11 degrees, 0.95 degrees, and 0.55 degrees. Further, the efficiency of the positioning method is improved by 510.4% and the comprehensive plug-in success rate is 95%. Therefore, the proposed CTMA in this paper can efficiently and accurately identify the CP while meeting the actual plug-in requirements. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.