# Research on the Body Positioning Method of Bolting Robots Based on Monocular Vision
## 6 Conclusions  
The experimental data were measured using the bolt robot test-bed and the error was analyzed. For the single visual bolt robot body positioning system, the following conclusions can be drawn:  
1. The monocular vision positioning system proposed in this paper could realize the function of fuselage positioning. The specific functions are as follows: (a) obtain image data information through the camera; (b) improve image quality through \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Distance of** & & & & & & & & & \\ \(Z\)**-axis/mm** & 0 & 700 & 1400 & 2100 & 2800 & 3500 & 4200 & 4900 & 5600 & 6300 \\ \hline
**Mean value/mm** & 2604 & 2603.6 & 2621.6 & 2655.2 & 2650.4 & 3628.6 & 2646.4 & 2649 & 2646 & 2647.4 \\ \hline
**Distance of** & 7000 & 7700 & 8400 & 9100 & 9800 & 10,500 & 11,200 & 11,900 & 12,600 & 13,300 \\
**Z-axis/mm** & 2635 & 2646.6 & 2642 & 2640.4 & 2633.2 & 2631 & 2641.8 & 2640 & 2621.6 & 2642.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Depth measurement of the experiment. image processing methods such as distortion correction, image enhancement, and image denoising; (c) realize the position detection of the bolt robot through the feature extraction algorithm, straight line fitting algorithm, and position estimation algorithm. In summary, the contrast of the image increases through equalization processing, the blur in the process of image movement is removed through the Wiener filtering algorithm, and the anchor end circle feature is effectively and stably extracted based on the Hough circle algorithm. Through simulation, the monocular vision positioning model was shown to be in the range of 100 m, and the positioning algorithm error was almost zero, which meets the positioning requirements of the anchor-drilling robot. 2. According to the analysis of the results obtained from the experiment, the effects of optical axis non-vertical error and depth error on the experimental results met the requirements. Through the analysis of the experimental results, the results obtained are as follows: (a) the single-point error of the comprehensive error was less than 8 mm, the comprehensive error of the \(x\)-direction and the \(z\)-direction was less than 100 mm, and the cumulative longitudinal tilt error was less than 1.6\({}^{\circ}\), which met the needs of the project. (b) The horizontal synthesis error and forward synthesis error obeyed normal distribution. The probability that the combined error was within 50 mm was more than 98%. (c) The longitudinal tilt error was only affected by the non-vertical optical axis. (d) The error was mainly caused by the error of the non-vertical optical axis, accounting for more than 95% of the error. In summary, the non-vertical error between the camera and the roof had a great influence, but the comprehensive simulation results showed that the error was still within the range of practical engineering requirements. In addition, there are some issues that need to be addressed here. First of all, in recent years, IMU has been used in the field of mobile robot positioning, but IMU has a high update frequency and high estimation accuracy in a short time. However, its long-term use will produce error accumulation, and the motion error increases with time, so it can only locate for a short time and provide relative positioning information. Its function is to measure the route of motion relative to the starting object, and it cannot provide specific information about its location. Additionally, it is often used with GPS, but the underground coal mine does not have the appropriate surface conditions, such as the lack of GPS, which will lead to the decline in IMU positioning accuracy. The method we designed is based on the arrangement of the anchor network, selects the bolt end as the feature, and uses vision to identify the feature points so as to obtain the current position of the robot.