# A Robust Localization System Fusion Vision-CNN
## 5 Experiments
### _Relocalization Experiment_
#### 5.2.2 Test of Relocalization Integrating Visual and Laser Information  
The relocation process of fusing vision and laser information is shown in Figure 17. Figure 17a is the robot in a certain test pose in the actual environment and Figure 17b is the image obtained by the camera. The coarse relocation result based on vision is shown in Figure 17c, and the accurate relocation result fused with laser information is shown in Figure 17d.  
We tested 11 points in the environment, each with eight directions, as shown in Figure 18a. The search window size of the algorithm was set as: translation direction \(-2.0\) m to \(+2.0\) m and angle range \(-0.785\) rad to \(+0.785\) rad. The system successfully performed accurate relocation in all test directions of all test points and the search time from the coarse pose estimated by vision to the fine pose fused with laser information is shown in Figure 18b. The average time of using posenetLSTM and the neural network proposed in this paper in all tests are 0.3261 s and 0.3602 s, respectively, an increase of  
\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) & 0.2738 & 0.1769 & 0.2686 & 0.1870 & 0.1370 & 0.1867 & 31.70\% & 22.56\% & 30.49\% \\ pose\_y(m) & 0.2758 & 0.2070 & 0.2751 & 0.2089 & 0.1359 & 0.2078 & 24.26\% & 34.34\% & 24.46\% \\ pose\_yaw(rad) & 0.0799 & 0.0545 & 0.0798 & 0.0584 & 0.0424 & 0.0571 & 26.91\% & 22.20\% & 28.45\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of the pose estimation errors in the \(x\), \(y\), and _yaw_ directions, respectively.  
Figure 17: (**a**) Robot at test position; (**b**) Image acquired by camera; (**c**) The coarse relocation result based on vision; (**d**) The accurate relocation result fused with laser information.  
9.46%. The improvement of computational efficiency is not obvious and this is because the main factor determining the system relocalization time is the search window size parameter of the algorithm, which is set to the same. The visual convolutional neural network proposed in this paper has a higher visual relocation accuracy, and the real-time performance of system relocalization can be greatly improved by reducing the search window in practical applications.  
The test results show the system's excellent precision relocation capabilities. For example, in test positions 1 and 9 facing the door, the image information obtained by the camera was very similar, and position 9 also contained chaotic environmental parts. The position of the two doors and the chaotic part are shown as A, B, and C in Figure 18a. However, the algorithms can successfully complete precise relocation, showing excellent precise relocation capabilities and the relocation process is shown in Figure 19.  
Figure 19: (**a–c**) are the images obtained by the camera, the coarse positioning of vision and the accurate relocation result fused with laser information at test point 1, respectively; (**d–f**) are the results of test point 9.  
Figure 18: (**a**) Location of 11 test points; (**b**) Search time from coarse to fine pose.