# A Framework of Wearable Sensor-System Development for Urban 3D Modeling
## 4 Evaluation of System Calibration Results  
In this section, the results of system calibration through proposed framework are presented and evaluated. First, the performance of the proposed point-plane correspondences-based approach is presented through precision and accuracy of sensor-bundle ROPs. Next, a visual analysis of the accuracy of ROPs between the estimated LiDAR and the camera is performed. Finally, the accuracy of ROPs between LiDAR is estimated via global optimization and then comparatively evaluated with other approaches. The results of system calibration for each sensor bundle using the proposed method were derived as presented in Table 2. In this study, the proposed approach was judged to exhibit high accuracy based on the standard deviation results, residuals of image control points, and correlation between orientation parameters. Table 3 presents the standard deviation of the estimated ROPs and the residuals of the image control points. The maximum standard deviation of estimated ROPs was found at X0 and \(\kappa\) of \(\text{rop}_{12}^{c2}\), which were 4.95 mm and 0.0119\({}^{\circ}\), respectively. Further, the RMSE of the image control points were found to be a maximum and minimum of 0.68 and 0.79 pixels, respectively. In addition, the correlation between the ROPs was generally low (Table 4). The highest correlation (0.95) was that between Y0 and \(\omega\) of \(\text{rop}_{11}^{c1}\); however, it was not at a level that had a significant effect on the accuracy of system calibration. Global optimization was performed using the estimated ROPs of each sensor bundle (Table 2) and the standard deviation of ROPs (Table 3). The results are presented in Table 5; where \(\text{ROP}_{11}^{c1}\) and \(\text{ROP}_{12}^{c1}\) show the corrected ROPs of cameras 1 and 2, respectively, and \(\text{ROP}_{11}^{c2}\) shows the ROPs of LiDAR 1 derived through global optimization. In this study, the fusion data generated using the final ROPs was analyzed to verify the accuracy of the final system calibration results. Figure 8: Test-bed for system calibration: (**a**) Calibration target, (**b**) test-bed. Figure 9: Configuration of calibration data acquisition. First, in this study, the accuracy of the final ROPs was analyzed through a visual analysis of the fusion accuracy between the camera image and the point cloud. To clearly determine the accuracy of the fusion data, a color that was easily identifiable was provided to the main plane of the image (a relatively wide plane with the edges of the plane easily  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**top\({}_{11}^{c}\)**} & \multicolumn{6}{c}{**top\({}_{12}^{c}\)**} \\ \cline{2-11}  & **X0** & **Y0** & **Z0** & \(\mathbf{\omega}\) & \(\mathbf{\varphi}\  
identifiable on the image), and the fusion accuracy was confirmed at the corners where each plane met. The fusion accuracy visual analysis was performed using sensor data acquired at two test-sites. Figure 10 shows the optical images acquired at the test-sites; Figure 10a-d are the original images obtained at test-site 1 and the images with new colors, (e-h) are the original images obtained at test-site 2 and the images with new colors. Figures 11 and 12 show the data fusion results for test-sites 1 and 2, respectively. As evident, in both test-sites, the color of the plane corresponding to the point cloud corresponding to each plane was properly assigned and overall accurate data fusion was achieved. Initially, at test-site 1, a data fusion error was confirmed at position 1. However, considering that the error appeared on both sides of the wall simultaneously, the error may have been due to a LiDAR multi-path error at the edge and not because of an ROP error.