# Adaptive Suppression Method of LiDAR Background Noise Based on Threshold Detection
## 4 Experimental Results  
In order to verify the practical effect of the method, the experiments were carried out based on the self-developed LiDAR of team. The LiDAR is controlled by an external program so that it can work in conventional mode, fast mode, and adaptive mode, respectively. Among them, the traditional mode only adjusts the APD bias voltage. In addition to adjusting the bias voltage, the fast mode also quickly adjusts the detection threshold voltage through the current steering architecture, and the adaptive mode adds the DAC feedback correction method on a fast mode basis. The diagram of the experimental scene is shown in Figure 10. The LiDAR adopts the same laser repetition frequency and power to detect the same area using the above three modes, respectively, and then the acquired point cloud of the target area is compared and analyzed. The main parameters of the LiDAR used in the experiment are shown in Appendix B Table A2. Although the comparison can be directly observed from the LIDAR point cloud acquired using different methods, this comparison is rough, because the noise accumulates over time in the experiment, and each scanning does not take exactly the same amount of time. In order to quantify and analyze the experimental data, this paper refers to the evaluation method in [34] and introduces the parameter noise ratio \(\mathrm{P_{m}}\), which is used as an \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time/a.m.** & **8** & **9** & **10** & **11** & **12** \\ \hline Solar altitude/\({}^{\circ}\) & 13.3 & 22.6 & 30.2 & 35.3 & 37.1 \\ \hline Illuminance/lux & 6387 & 12,960 & 22,120 & 27,010 & 32,550 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Solar height and illuminance data. Figure 10: Diagram of LiDAR experiment scene. Figure 9: Relationship between detection threshold and FAR. objective measure of the background noise suppression effect by calculating the ratio of noise to itself in the point cloud. The definition of the noise ratio \(\mathrm{P_{m}}\) is as follows:  
\[\mathrm{P_{m}}=\frac{N_{ns}}{N_{og}}=\frac{N_{og}-N_{de}}{N_{og}} \tag{20}\]  
where \(\mathrm{N_{og}}\) is the number of original point clouds, \(\mathrm{N_{de}}\) is the number of denoised point clouds, and \(\mathrm{N_{ns}}\) is the number of noise points. The laser radar detection experiment was carried out on a clear night, and the point cloud was collected as the basic data, as shown in Figure 11a. The original point cloud is denoised by the point cloud denoising program in the MATLAB, and the result of point cloud data processing is shown in Figure 11b. Then, the original point cloud and the denoised point cloud data are subtracted to obtain the noise points:  
\[\mathrm{P_{m}}=\frac{N_{og}-N_{de}}{N_{og}}=\frac{1944944-1944879}{1944944}=3.34 \times 10^{-5} \tag{21}\]  
According to Formula (21), the noise ratio of the LiDAR at right is about 0.003%. LiDAR detection experiments were conducted multiple times during clear daylight hours. The illuminance values at that time were measured by UNI-T's UT383 illuminance meter, while the local solar height at the corresponding time of the day was queried on the Internet, and the recorded data are shown in Table 1.  
From 8:00 to 12:00, each time with an interval of one hour, the LIDAR was controlled to work in three modes for detection experiments, and the collected point cloud data at 8 a.m., 10 a.m. and 12 a.m. are shown in Figure 12. From the three-dimensional point cloud image, it can be seen that the noise in the traditional mode is the highest in the daytime, and it increases with the enhancement in background radiation. Compared with the traditional mode, the data of point cloud by using the fast mode has less noise, and the suppression effect is better than the former under strong background radiation. Compared with the above two modes, the noise of point cloud by using the adaptive mode is the lowest, and the noise suppression effect is the best under strong background radiation. The noise of point cloud is analyzed according to Equation (20).