# A Framework of Wearable Sensor-System Development for Urban 3D Modeling
## 4 Evaluation of System Calibration Results
At test-site 2, an error appeared at position 2 in Figure 11b, and the level of error was approximately 1.5 cm at a distance of approximately 4 m. However, as the error appeared along the edge of the boundary between the white and yellow planes, it did not appear significantly to affect the overall accuracy of data fusion. Figure 11: Optical camera-LiDAR data fusion results for the wearable system at (**a**) test-site 1, (**b**) test-site 2. Figure 10: Image data for visual analysis of system calibration results: (**a**) original image of camera 1 at test-site 1, (**b**) original image of camera 2 at test-site 1, (**c**) new colored image of camera 1 at test-site 1, (**d**) new colored image of camera 2 at test-site 1, (**e**) original image of camera 1 at test-site 2, (**f**) original image of camera 2 at test-site 2, (**g**) new colored image of camera 1 at test-site 2, (**h**) new colored image of camera 2 at test-site 2. To clarify the accuracy of ROPs (ROP\({}_{l1}^{I2}\)) between the LiDARs, fusion error between point clouds was evaluated based on the distance between the point clouds being acquired from LiDARs 1 and 2, which correspond to the same plane in this study. With more accurate ROPs between the LiDARs, the planes scanned simultaneously by both LiDARs can be visualized at the same plane through data fusion. Conversely, with less accurate ROPs, each plane exhibits different plane shapes and the distance between the planes becomes larger. Figure 12 shows the calculation methodology for the fusion error between point clouds. The gray and green points are the point clouds of LiDAR 1 and LiDAR 2, respectively, and the black lines are planar patches extracted from the point clouds of LiDAR 1. In addition, \(P_{i}\) is the i-th point cloud of LiDAR 2, \(D_{i}\) is the distance (m) between LiDAR 1 and \(P_{i}\), and \(e_{i}\) is the distance from \(P_{i}\) to the planar patch. The fusion accuracy between LiDARs was evaluated based on the mean bias error (MBE), the mean absolute error (MAE), and the root mean square error (RMSE) of \(e_{i}\) and \(\widetilde{e_{i}}\); \(\widetilde{e_{i}}\) was calculated using Equation (5), implying that \(e_{i}\) was normalized based on the distance between the reference sensor (LiDAR 1) with \(P_{i}\) set as 1 m.  
\[\widetilde{e_{i}}=\frac{e_{i}}{D_{i}} \tag{5}\]  
In the accuracy evaluation of ROPs between LiDARs, ROPs obtained through the proposed method (ROP\({}_{l1}^{I2}\)) were comparatively evaluated with nominal ROPs and ROPs derived through ICP (iterative closest point). Table 6 presents the nominal ROPs (ROP\({}_{nom}\)) used in the wearable system design and ROPs obtained through ICP (ROP\({}_{icp}\)). To evaluate the accuracy of ROPs between LiDARs, planes were used located in the section where the FOVs of LiDARs 1 and 2 overlapped. Figure 13 shows the positions of the used planes (No. 1-4). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **X0 (mm)** & **Y0 (mm)** & **Z0 (mm)** & \(\mathbf{\omega}\) (\({}^{\circ}\)) & \(\mathbf{\varphi}\) (\({}^{\circ}\)) & \(\mathbf{\kappa}\) (\({}^{\circ}\)) \\ \hline ROPs\({}_{nom}\) & 0.00 & \(-\)350.00 & 400.00 & 60.00 & 0.00 & 0.00 \\ \hline ROPs\({}_{icp}\) & 10.50 & \(-\)341.00 & 415.00 & 59.10 & 1.49 & \(-\)1.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Used ROPs for comparative evaluation of LiDAR data fusion accuracy. Figure 12: LiDAR data fusion error calculation method. Table 7 lists the fusion error between point clouds based on ROP type and test-site location, confirming that the most accurate ROPs were derived employing the proposed methodology. The overall error based on the RMSE was the lowest for ROPs\({}_{11}^{l2}\), followed by ROPs\({}_{nom}\) and ROPs\({}_{icp}\). In particular, the fusion data using ROP\({}_{11}^{l2}\) derived using the proposed method showed the lowest fusion error at all positions.