## 1 Introduction
However, the vision-based docking approach is prone to calibration errors, as demonstrated in [17], where a Faster R-CNN algorithm was used to detect arbitrary visual markers. The pose of the mobile robot was estimated using the solvePnP algorithm, which related the 2D-3D point pairs. However, the solvePnP algorithm gave systematically inaccurate pose estimates in the x-direction and, hence, proved to be ineffective for docking. Laser range finder techniques usually detect the charger based on the uniquely manufactured shape of the charging station to distinguish it from surrounding objects. One such example is the V-shaped recess on the MiR (mobile industrial robot) [18] made by Fetch Robotics, which required the charger to be placed separately from any laser-height obstacles to enable the successful detection of the contour of the charger using the laser range finder. However, the requirement of a special shape adds to a charger station's fabrication costs and limits mobile robots' practical applications in unstructured environments. To solve this problem, a self-adhesive reflective tape can be used to help the robot identify the charger, as reported in [19]. When using this reflection detection technique, the charger was easily distinguished from other similar objects in an unstructured environment, as verified by extensive experiments. Moreover, Lidar can be used for obstacle detection and avoidance,navigation, and pose estimation in a mobile robot without the use of additional hardware. In [20], a multi-sensor fusing method used intensity and range data fusion, with a covariance intersection approach, to estimate the robot pose during docking and recharging. Using the inverse perspective projection method, an artificial landmark was employed as a visual cue on the charging station to be identified by the robot. Then, based on the laser range data, the geometrical relationship between the robot and charger station was estimated precisely using the covariance intersection method. In [21], automated guided vehicle (AGV) autonomous docking was investigated in an unstructured environment with human presence. An autonomous docking technique was implemented with a non-visual sensor, such as Lidar or AprilTag, for charger detection. A deep learning network was used to detect and recognize humans and objects. Practical experiments verified that the AGV could co-exist with humans and perform autonomous docking in unstructured environments. With the development of deep learning techniques, deep-learning-based approaches perform better in autonomous docking applications. In [21], the MobileNetv2-SSDLite deep learning framework was adopted to detect and recognize a specific person in the human-robot collaborative environment. Once the particular human was identified, the robot system could achieve automatic docking to the target person based on LiDAR and a RGB-D camera. Given that high-resolution images from a camera can provide rich information, in [22], the authors proposed a fusion method to make use of images from a camera to enrich the raw 3D point clouds from LiDAR. The sparse convolutional neural network was adopted to predict the dense point clouds to enrich the raw point clouds and then employed to execute LiDAR SLAM. In [23], the Faster-RCNN model with a MobileNetv3-Large FPN backbone was used to detect arbitrary dynamic obstacles and identify the charging station. It was proven that it can distinguish the charging station from other surrounding objects in most scenarios. Previous studies indicate that the autonomous docking and recharging process becomes more reliable and repeatable when using a multi-sensor fusion approach in both structured and unstructured environments. However, IR sensors require specific configurations, such as signal receivers, which are inconvenient and incur high costs [9]. Therefore, most existing fusion methods consider combining the Lidar sensor with computer vision techniques because of their low costs and non-destructive abilities.