## 6 Discussion and Conclusions  
This paper discusses the challenges faced by current autonomous docking and recharging methods in the context of mobile robots in manufacturing environments. Current state-of-the-art methods heavily rely on Lidar, which makes it expensive and time-consuming for mobile robotic systems to achieve autonomous docking and recharging applications. Therefore, a Lidar and vision data fusion method, generated by combining deep learning object detection and Lidar-based docking approaches, was proposed to address the aforementioned problems. A YOLOv7-based real-time object detection model was developed to identify wireless chargers. To evaluate the developed detection method, a set of testing images and real-time video frames captured through a Division camera was used, and it achieved an average of 95% accuracy. The performance of the detection model for the charging station was compared with that of existing methods. According to the comparison results, the proposed method outperformed the other methods. A Lidar and vision data fusion approach was then developed to localize the wireless charger and then navigate the mobile robot to achieve docking to the charging station, reducing the computation costs of the system. Despite the advantages of the proposed method, it is limited by some challenges. For instance, the wireless charging station needs to be in an enclosed space, which can be used to calculate the wall_laser distance in the proposed method. Moreover, the developed charging station detection method can be affected by the low-illumination conditions in the manufacturing environment and by the blurring caused by the unstable movement of the mobile robot.  
So far, this proposed Lidar-camera data fusion method for autonomous docking and recharging has only been validated on a 2D camera and a Lidar system. Future work will focus on the use of a stereo camera and Lidar system to improve the performance of the developed method in a practical autonomous manufacturing environment. Furthermore, for the docking procedure itself, to improve the pose estimation of the robot in relation to the charger, calibration between the vision and Lidar data needs to be implemented in future work.  
All authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by [PERSON] The first draft of the manuscript was written by [PERSON] and [PERSON], and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Example of real-time charging station detection.  
**Funding:** This research was funded by the Ministry of Economic Development, Trade, and Tourism of the Government of Alberta through the Autonomous Systems Initiative of the Major Innovation Funds, and the Go Productivity funding. The authors also would like to acknowledge the NSERC (Grant Nos. NSERC RGPIN-2017-04516 and NSERC CRDFJ 537378-18) for further funding this project.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are unavailable due to privacy concerns.  
**Acknowledgments:** We express our gratitude to the Ministry of Economic Development, Trade, and Tourism of the Government of Alberta for funding this project through the Autonomous Systems Initiative of the Major Innovation Funds, and the Go Productivity funding. The authors also would like to acknowledge the NSERC (Grant Nos. NSERC RGPIN-2017-04516 and NSERC CRDFJ 537378-18) for further funding this project. The support of [PERSON] at Imperial Oil Limited in carrying out the work is gratefully acknowledged.  
**Conflicts of Interest:** The authors declare no conflict of interest.