# Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
## 4 Experimental
### Field Experiment in A Coal Mine  
We conducted a field experiment in a coal mine in Pingdingshan, China, to evaluate the proposed tracker. The experimental platform was the ZDY4000 LK coal mine drilling robot developed by the China Coal Technology Engineering Group (CCTEG) at the Xi'an Research Institute. The experimental environment and hardware configuration are shown in Figure 13. The experiment was conducted at the air shaft of a ventilation roadway in a coal mine, where the lighting conditions were poor. An intrinsically safe (Ex i) three-dimensional lidar sensor, an Ex i monocular camera, and two flameproof (Ex d) monocular cameras were installed at the front of the robot. Two of the cameras faced the wall, and one was pointed away from the robot. The forward-facing camera was used for real-time image acquisition, and the data were stored in the rosbag format. Lidar collects 3D point cloud to provide navigation information for a robot. The image resolution was 1280 \(\times\) 720 pixels, and 940 images were acquired. The video data contained challenges such as light changes, out-of-plane rotation, out-of-view, and fast movement. In the field experiment, we use the C++ version of the algorithm and it runs in the ROS. The target area is manually selected in the camera interface. The parameters are slightly adjusted. We changed the search area shape as \"square\" instead of \"proportional\", and the search area scale was set as 5. The CSRM confidence was enabled. Other parameters are consistent with the dataset evaluation. Figure 14 shows the tracking results of the LLCT, baseline trackers (LCT [18], fast DSST [39]), and the SOTA (BACF [12], ECO-HC [14], ADT [36]) for tracking a dynamic object in the downhole environment. The proposed tracker outperforms most of the SOTA trackers under these conditions. In the first 450 frames, all algorithms can track the moving target accurately, but some trackers show offsets (e.g., LCT and fast DSST). When the target rotates out of the plane around the 500 th frame, the LCT and fast DSST lose the target, and a target offset occurs in the BACF. When the target moves out of view in the 600 th frame, the LLCT, BACF, and ECO-HC remain at the position where the target disappears. After the target reappears in frame 635, ADT and LLCT reacquire the target. Overall, LLCT performed better than ADT. Table 4 shows the quantitative results, which indicate that the tracking success rate of the LLCT is higher than that of the other trackers. Figure 13: Field experiment in a coal mine roadway. (**A**) shows the roadway environment and the location of the drilling robot. (**B**) shows the location of the external sensors, which meet the underground explosion-proof requirements. Figure 15 shows the comparison of the predicted trajectory obtained from the LLCT tracker and the actual trajectory. The trajectory tracking strategy refers to MACF [42]. In the experiments, the depth information is replaced by the scale information of each frame, and the initial scale is set to \(S_{\text{init}}(n)=1.0\), i.e., \(n=0\). A larger \(S_{t}(n)\) value means that the target is closer to the robot. The blue line represents the track fitted by the benchmark center point, the yellow line is the track fitted by the LLCT at the center of the tracking BB, and the red line is the filtered track obtained from a Kalman filter. The results in the 2D plane and 3D space indicate that the LLCT tracker accurately predicts the position and scale of moving targets in a static background. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Tracker** & **LLCT** & **f-DSST** & **LCT** & **ECO-HC** & **BACF** & **ADT** \\ \hline FPS & 36.6 & 154 1 & 51.8 & 56.7 & 52.4 & 35.6 \\ DP & 80.8\% & 8.72\% & 55.9\% & 57.1\% & 47.7\% & 48.2\% \\ OP & 65.3\% & 7.34\% & 48.2\% & **43.9\%** & 38.1\% & 52.8\% \\ \hline \hline \end{tabular}  
* Red: the best; Green: the second; Blue: the third. OP: average overlap score with the threshold of 0.5, DP: average center location error with the threshold of 20 pixels.  
\end{table}