# Mapping Server Collaboration Architecture Design with OpenVSLAM for Mobile Devices
## 4 Experiments and Results
### Experimental Results  
The captured image was saved as Frames.m4v. cam0 was converted to an image based on the timestamp (Frames.txt). GPS.txt was the saved location data. Accel.txt was the acceleration data. Gyro.txt was the angular velocity data. IMU.txt was the saved inertia data obtained by calculating the acceleration data and angular velocity data. config.yaml contained the camera settings. The sparse map (.bin) and sparse point cloud (.ply) files were created through the Unity engine with the image data and IMU data captured by the mobile camera. Table 5 is a description of the main files. Figure 9 is the result of comparing the partial capture of a point cloud map that matches the actual indoor space by capturing the indoor space of Figure 8 for about 2 min and 30 s in advance. The resolution was 1280 \(\times\) 720, and point clouds in the captured indoor space numbered about 54,000. The average fps was measured to be 40, and the battery consumption was very low, about 1% or less (Table 6). \begin{table}
\begin{tabular}{c c} \hline
**Filename** & **Description** \\ \hline
**Frames.m4v** & captured image \\
**Frames.txt** & time stamp \\
**cam0** & image converted form Frames.m4v based on Frames.txt \\
**GPS.txt** & pose data \\
**Accel.txt** & acceleration data \\
**Gyro.txt** & angular velocity data \\
**IMU.txt** & inertia data \\
**config.yaml** & camera setting value \\
**Sparse\_Map\_Unity.bin** & sparse map created in Unity engine \\
**Sparse\_PointCloud.ply** & sparse point cloud \\ \hline \end{tabular}
\end{table} 
Table 5: SLAM performance-related files. Figure 8: Indoor environment for mobile SLAM system evaluation. The overall consistency of the trajectory estimated in the visual SLAM system is an important factor. The absolute trajectory error (ATE) is evaluated by comparing the absolute distance between the estimate and the actual measurement. Since both trajectories can be specified in any coordinate frames, alignment is required first, in which the method of Horn [33] to find the rigid transformation, S, corresponding to the least-squares solution mapping the estimated trajectory, \(P_{1:n}\), to the actual trajectory, \(Q_{1:n}\), can be used. The ATE at time step \(i\) can be calculated as follows [34]. \[F_{i}:=Q_{i}^{-1}SP_{i} \tag{1}\]  
For each temporal index of the converted components, the root-mean-squared error (RMSE) can be calculated as follows. \[RMSE(F_{1:n}):=\left(\frac{1}{n}\sum_{i=1}^{n}\parallel trans(F_{i})\parallel^ {2}\right)^{1/2} \tag{2}\]  
The accuracy of the localization was evaluated by comparing it with the ground-truth data for the monocular of the EuRoC dataset, and the comparison results are shown in Table 7. The average RMSE of the monocular mobile SLAM is about 2.017, but it is not a problem for the purpose of displaying AR contents through a mobile app. \begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Description** \\ \hline
**Display Resolution** & 1280 \(\times\) 720 \\
**Recording Time (s)** & 150 \\
**Number of Points** & about 54,000 \\
**Frame rate (FPS)** & avg. 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mobile SLAM system performance. Figure 9: Map creation results by mobile SLAM system. Figure 10 is a picture comparing the results before and after the optimization. When loop closure was detected, the point cloud map was optimized close to the scale of the real space centered on the current location, as shown in Figure 10. Since the point cloud map represents a sparse map reconstructed with a monocular system, it may be difficult to identify objects with the naked eye. For such a problem, it is necessary to maintain the color of the point cloud and improve it so that it can be reconstructed as a close point cloud map. In addition, if the map is taken using a sensor that can obtain depth data, such as a stereo camera or RGB-D camera, it is expected that mapping and localization will be more accurate.