# A Robust Localization System Fusion Vision-CNN
## 4 Simulation
### Initialization of the Robot Global Positioning
#### 4.2.2 System Relocalization Analysis  
The experiment in this section is to verify the ability of the algorithm to perform accurate global relocation when the initial position of the robot is unknown. The algorithm's default initial position of the robot is at the origin of the coordinate system, which is \((x_{0}^{map\_base},y_{0}^{map\_base},\varphi_{0}^{map\_base})=(0.0,0.0,0.0)\). The linear and angular search ranges of the algorithm are \((-1.5\) m, \(+1.5\) m) and \((-\pi\) rad, \(\pi\) rad), respectively. It is time-consuming to calculate the scores of all laser points in each particle state. To improve the calculation time efficiency, the score is calculated after every eight laser points and approximately 70 laser points are calculated for one frame of data for scoring. From the simulation experiment results in the previous section, the accuracy of the pose estimated based on the neural network was poor. Here, the pose provided by the neural network was used as the initial pose, and the laser information was used to estimate the precise pose of the robot. A total of \(36*4=144\) sets of tests were done, including 36 positions, with each position including 4 test directions. The relocalization test results based on posenetLSTM and the method proposed in this paper are shown in Figure 8. The green arrow in Figure 8 indicates that the relocalization test was successful (the exact pose of the robot could be correctly estimated), and the red arrow indicates that the estimation cannot be successful. The blue arrow in Figure 8 indicates that the method proposed in this paper can be successfully estimated but cannot be estimated successfully based on posenetLSTM. Based on the method proposed in this paper, the number of successful relocalizations is 118 with a success rate of 81.9%, and the number of successful relocations based on posenetLSTM is 103 with a success rate of 71.5%. It can be seen from Figure 8 that most of the relocalization failures are near obstacles because the camera's viewing angle is almost blocked in such cases. The second reason for the failure of relocalization is that the surface of some obstacles in the simulation environment has repeated brick-shaped textures, which is not conducive to the relocalization of the visual neural network. The pose change under the condition that both the proposed method and posenetLSTM can relocate successfully is shown in Figure 9. For the yaw angle in Figure 9,f, the \(-\pi\) rad and \(+\pi\) rad represent the same orientation. At the same time, when the relocalization is successful, the two methods can both achieve extremely high positioning accuracy after optimizing the initial pose estimated by the visual neural network using laser information. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**posenetLSTM**} & \multicolumn{3}{c}{**The System**} & \multicolumn{3}{c}{**Improvement**} \\ \cline{2-10}
**Pose** & **RMSE** & **MAE** & **S.D.** & **RMSE** & **MAE** & **S.D.** & **RMSE** & **MAE** & **S.D.** \\ \hline pose\_x(m) & 0.5938 & 0.4642 & 0.5939 & 0.4291 & 0.3191 & 0.4291 & 27.74\% & 31.26\% & 27.75\% \\ pose\_y(m) & 0.5595 & 0.4094 & 0.5586 & 0.3187 & 0.2345 & 0.3148 & 43.04 & 42.72\% & 43.64\% \\ pose\_yaw(rad) & 0.1840 & 0.1180 & 0.1831 & 0.09518 & 0.06790 & 0.09197 & 48.27\% & 42.45\% & 49.77\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The result of the metric absolute speed error when robot is in motion. Figure 8: Record result of relocation (**a**) The posenetLSTM method; (**b**) The method proposed in this paper. Figure 9: The change of pose from coarse to fine in the process of relocalization that combines laser and visual information. **(a,c,e)** The pose change in the \(x\), \(y\), and \(yaw\) directions, respectively, when using the posenetLSTM method; **(b,d,f)** The pose change in the \(x\), \(y\), and \(yaw\) directions, respectively, when using the method proposed in this paper.