## 5 Experiments and Discussion
### Experiment Comparison of Different Devices
The results are shown in Table 3, in which it can be seen that the method proposed in this paper constructs a 3D point cloud of a row of trees; the relative error of the tree spacing is 4.65%, and the relative accuracy can reach 95.35%. At the same time, this paper also selects a trapezoidal forest area for study and measures its short base, long base, altitude, lateral side, and the number of trees. The coverage area is approximated by the trapezoidal area. By analyzing the data in Table 4, it can be seen that the relative error of the method proposed in this paper is 1.92% in part of the forest, and the relative accuracy can reach 98.08%. As shown in Figure 7, the 3D point cloud maps constructed using different sensors are compared with the actual scene, and they can completely reflect the abundant environmental information in the forest areas. Furthermore, it can be seen from the data in the above four tables that the algorithm proposed in this paper has good measurement results for tree height, tree diameter at breast height, and tree spacing and distribution in forest areas. Moreover, the measured average relative error of the algorithm in this paper is less than 5% compared with real environmental values, while the measured average relative error of the multi-beam lidar is about 2%. The accuracy of the maps created using the two methods is very similar, but the cost of the proposed algorithm is less than one-tenth of that of the multi-beam lidar. In addition, the point cloud registration algorithm based on the LIO-SAM of the multi-beam lidar has high complexity. It takes more time compared with real-time reconstruction based on the visual-IMU fusion of a single-line lidar proposed in this paper. Considering that forest development pays more attention to the distribution of trees, while the reconstruction of branches and leaves are not key factors, the algorithm proposed in this paper achieves better real-time performance and lower price at the cost of losing parts of branches and leaves in the point cloud data. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Part of the Forest**} & \multicolumn{3}{c}{**Short Base**} & \multicolumn{3}{c}{**Long Base**} & \multicolumn{3}{c}{**Altitude**} & \multicolumn{3}{c}{**Lateral Side**} & \multicolumn{3}{c}{**Average Error**} & \multicolumn{1}{c}{**Coverage Area**} \\ \cline{2-11}  & \(D_{l}\)**(m)** & \(\delta\) **(\%)** & \(D_{l}\) **(m)** & \(\delta\) **(\%)** & \(D_{l}\) **(m)** & \(\delta\) **(\%)** & \(D_{l}\) **(m)** & \(\delta\) **(\%)** & \(\delta\) **(\%)** & **(m\({}^{3}\))** \\ \hline Real value & 25.3 & 0 & 35.8 & 0 & 24.5 & 0 & 26.6 & 0 & 0 & 748.47 & 32 \\ \hline Multi-beam lidar & 24.9 & 1.58 & 35.2 & 1.68 & 24.9 & 1.63 & 27.1 & 1.88 & 1.69 & 748.25 & 32 \\ \hline Single-line lidar & 24.8 & 1.98 & 35.3 & 1.40 & 25.0 & 2.04 & 27.2 & 2.26 & 1.92 & 751.25 & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Measurement data of some forest areas. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Height (m)**} & \multicolumn{6}{c}{**Different Devices**} \\ \cline{2-7}  & \multicolumn{3}{c}{**Multi-Beam Lidar**} & \multicolumn{3}{c}{**Single-Line Lidar**} \\ \cline{2-7}  & **RMSE (cm)** & **Bias (cm)** & **Pearson’s \(r\)** & **RMSE (cm)** & **Bias (cm)** & **Pearson’s \(r\)** \\ \hline
1.25 & 2.92 & 0.439 & 0.987 & 4.47 & \(-\)0.865 & 0.947 \\ \hline
1.58 & 2.47 & 0.620 & 0.987 & 4.08 & 0.013 & 0.945 \\ \hline
1.86 & 1.69 & 0.370 & 0.984 & 4.49 & 0.016 & 0.938 \\ \hline Average & 2.36 & 0.476 & 0.986 & 4.35 & \(-\)0.273 & 0.944 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy assessment for DBH parameter.