## 1 Introduction  
With the continual rise in labor costs, there is an increasing demand for automation and intelligence across various industries. This has led to the widespread application of robots, especially in tasks requiring mechanical arm docking operations. Currently, unmanned refueling robots have become a hot research topic for many companies. While research on the actuators of refueling robots is already quite comprehensive and the technology relatively mature and stable, studies on the visual guidance and positioning of unmanned refueling robots are notably lacking. The visual guidance and positioning of refueling robots primarily involve two key technologies: object detection and pose estimation. Traditional object detection methods extract features from images using techniques such as Haar [1], SIFT [2], SURF [3], HOG [4], etc., followed by the use of classifiers like SVM [5], Adaboost [6], etc., to determine whether these features belong to the target object. However, with the development of deep learning, methods based on this approach have become mainstream. Currently, this includes Faster R-CNN [7], Mask R-CNN [8], DETR [9], Swin Transformer [10], YOLO series [11], and others. The precision of pose estimation directly impacts the docking with the target, with the most critical algorithm being the PnP (Perspective-n-Point) algorithm. Solution methods include P3P [12], DLT [13], Bundle Adjustment [14], and others. Later, [PERSON] and others proposed the EPnP (Efficient Perspective-n-Point) [15] algorithm based on the PnP pose estimation method, which offers high computational efficiency and reduced time complexity. Subsequent developments such as OPnP [16], UPnP [17], and RPnP [18] further improved the accuracy of pose estimation. With deep learning becoming a mainstay in image processing, an increasing number of deep learning methods are being used to address pose estimation issues. These include direct estimation methods like PoseNet [19], SSD-6D [20], Deep-6 DPose [21], PoseCNN [22], and keypoint methods such as BB8 [23], YOLO-6D [24], PVNet [25], etc. [PERSON] of South China University of Technology [26] and others used traditional object detection methods based on CCD binocular cameras to design a visual positioning system that can locate the space of a fuel tank cap. [PERSON] from Jilin University [27] and others were among the first to use deep learning methods to detect vehicle fuel tank openings, thereby enhancing the accuracy of object detection. [PERSON] and others proposed a vision-assisted connector cone-style autonomous aerial refueling scheme for drones [28], aiming to accurately acquire the relative pose information between the refueling plug and cone during autonomous aerial refueling of drones. [PERSON] and others designed a robotic refueling system for refueling special equipment such as naval ships at sea [29]. This refueling robot consists of two parts: one is the visual guidance part for precise positioning of the target fuel tank, and the other is the execution part, a soft pneumatic arm with a magnetic end effector designed for transferring fuel, providing compliant and safe docking with unmanned surface vehicles. This paper targets a special fuel tank with non-standard connectors as the object, focusing on specific operational scenarios during the automatic refueling process of a particular vehicle. In real refueling operations, due to variations in the parking positions of the vehicles to be refueled and changes in vehicle posture, precise identification and positioning of the special vehicle's fuel tank opening is required before each refueling docking task. The mechanical arm must ensure that the end effector's orientation aligns with the plane normal vector of the fuel inlet. Based on this, the paper proposes a six-degree-of-freedom refueling mechanical arm docking method guided by RGB-D vision. This method uses an RGB-D camera to perform object detection and pose estimation of the target connector, providing target points for the mechanical arm.