## 1 Introduction
To improve the model's generalization performance, it is necessary to use anchor hole images from different roadway environments during the training process; however, these images need to be collected during the roof support process, making the collection of a large and diverse set of images a challenge. Consequently, the large-scale application of deep learning-based anchor hole localization methods in different roadway environments is difficult, and their generalization performance is poor. Due to its non-contact nature [13], real-time capabilities [14], and high accuracy [15], visual positioning technology has been increasingly applied in industries such as robotic navigation and autonomous driving [16; 17]. [PERSON] et al. [18] proposed a method by combining YOLOv8 network recognition with a monocular ranging method to achieve target localization and grasping for NAO robots, and the advantage in measurement accuracy was confirmed via experiments. [PERSON] et al. [19] proposed a novel tightly coupled monocular-inertial-pressure (IP) sensor fusion method for the underwater localization of a biomimetic robotic manta, and the experimental results demonstrated the effectiveness of the proposed method in terms of significantly improving both the position and attitude estimation of the biomimetic robotic manta. Monocular visual odometry was proposed through the use of template matching and IMU, which is very important in the navigation and positioning of mobile robots [20]. A method of identifying target objects by positioning marked targets with monocular vision was developed, and the results showed that it could achieve precise visual positioning, meeting the industrial application requirements for mobile robot operating platforms [21]. [PERSON] et al. [22] proposed a novel accurate positioning method based on vision and tactile sensors for object pose estimation in robotic manipulation, and the experiments achieved positioning accuracies of 0.138 mm and 0.203\({}^{\circ}\), meeting the demands for precise robotic positioning and manipulation. In recent years, the application of vision-based positioning methods in the coal mining field has gradually increased. [PERSON] et al. [23] introduced a binocular vision positioning method for the anchor holes in coal mines, and the positioning accuracy was distinctly enhanced when compared with manual operation. [PERSON] et al. [24] completed research on the use of image recognition technology during the automatic drilling of anchor rod drilling machines, intending to achieve automatic and accurate recognition and positioning of the hole coordinates during the anchor rod support process. [PERSON] et al. [25] proposed an intelligent identification and positioning method for roadway anchor hole positioning based on visual images and laser point cloud fusion, resulting in a position error of 3 mm; the average recognition time of the system in the case of a single anchor hole is 0.77 s. Vision-based positioning methods for anchor holes can achieve precise localization, and image processing techniques [26; 27] can effectively mitigate the adverse effects of lighting variations. This approach is suitable for anchor hole localization in most coal mine roadway environments, thus exhibiting strong generalizability. However, the existing methods only calculate the three-dimensional spatial coordinates and cannot determine the spatial orientation of the anchor holes. During the anchor bolt support process, the anchor bolt must be perpendicular to the plane of the anchor hole. Therefore, determining the spatial orientation of the anchor holes is significant in automated drilling and anchoring. To solve the challenge of detecting the spatial orientation of the anchor holes and to promote the development of drilling and anchoring robots in the direction of automation and intelligence, this study proposes a monocular vision-based anchor-hole-positioning method and builds a visual measurement system for anchor hole positioning.