## 2 Related Work  
AMRs with an upper device, such as a rack, roller, or lifter, are designed to pick up and deliver products between different workstations. The fundamental challenge of this task can be summarized as an indoor Simultaneous Localization and Mapping (SLAM) problem. While a considerable amount of research has been published on SLAM problems, state-of-the-art studies indicate that centimeter-level positioning can be largely solved for AMRs equipped with wheel encoders and laser scanners [8]. However, applications such as cargo delivery require AMRs to align with a roller conveyor with millimeter-level positioning precision and minute-level orientation precision, which is defined as a docking problem. [PERSON] and [PERSON] [9] briefly defines an AMR docking problem as arriving and stopping at a position relative to another object. Docking includes positioning a mobile chassis or onboard equipment relative to another object, such as a ray station, trailer, or pallet. Docking occurs at the final stage of navigation, using various sensors to update the target location and approach the target at a relatively low speed. The literature reports several docking techniques, which can be classified according to the type of sensor used, such as camera-based [10; 11; 12; 13], laser-based [14; 15; 16], and optics-based [17] sensors, among others [18; 19; 20]. One docking method is a laser-based technique that uses a 2D LiDAR scanner to detect specific artificial features for positioning. Commonly used features are reflectors or 3D shape blocks, which can make the docking target stand out from the background. [PERSON] [15] investigated different shape-based blocks used for precise AMR docking and found that L-shape blocks enable the most accuracy, with a repetitive error of \(\pm 3\) mm. However, the installation position of the blocks and features such as ambient corners can significantly affect this method. A similar technique is widely used in industrial robots, such as MiR and Fetch Robotics, for charge docking. [PERSON] [14] presented the development of a laser reflection intensity-based docking approach using reflective tape instead of a block installed on the charging station. The experiment showed that the proposed method had significantly higher success rates than a shape-based technique in unstructured environments. Reflective tape is also more accessible to deploy than 3D shape blocks. While a 2D LiDAR scanner is commonly used in docking methods, it may not be effective in actual industrial environments where workstation surface materials have a reflection intensity similar to reflective tape, leading to imprecise docking target detection. To address this challenge, several studies have attempted to use a LiDAR scanner for precise indoor localization without environmental modification. The fundamental idea of these techniques is to improve the point cloud registration method accuracy, such as the iterative closest point (ICP) method [16; 21]. The improved ICP approach meets the sub-centimeter precision demand. However, there has been no detailed analysis of the effect of dynamic elements on position accuracy, which is common in industrial scenarios due to product delivery and frequent worker movement. A camera, compared to a 2D LiDAR scanner, is more cost-effective and has higher information density. Previous studies have focused extensively on vision-based docking methods, with Quick Response (QR) codes and fiducial markers such as ArUco [22] or AprilTag [23] being widely used as visual landmarks to guide an AMR in docking tasks. Visual landmarks can be installed on the ground or side. In ground applications, a camera observes the landmarks from a parallel perspective. The detection algorithm needs to locate the landmark center point and its rotation around the z-axis, and it is possible to transform coordinates expressed in pixel coordinates to length coordinates using a pixel-to-meter ratio [12; 24].