# A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images
## 5 Experiments
### Results of the Fine Localization Stage
#### 5.2.3 Improvement of the Regression CNNs  
As presented above, after analyzing the results output by the regression CNNs, the networks of some of the rooms are not able to provide successful position estimations. Therefore, the aim of the present subsection is to propose some additional operations to improve the results. This subsection focuses on improving the regression CNNs related to the corridor and the office, since their related results were the worst among all the rooms within the environment. First, regarding the office, Figure 5 shows that the trajectory in this room is mainly distributed along the \(Y\)-axis (and therefore, the error along this axis is relative high, as shown in Table 3). Taking this fact into account, we propose splitting this room into two zones. An automatic splitting has been used by applying a spectral clustering approach in a similar way as it was done in [48]. After addressing the split, two regression CNNs were independently trained to carry out the position estimation task in each of the two zones. As for the room retrieval, two alternatives can be given: either retraining the classification CNN considering the office as two independent rooms (i.e., considering six rooms in total), or applying an intermediate step which retrieves the proper part of the office after retrieving the room and before estimating the robot position within the room. The results obtained are shown in Figure 6. From it, the conclusion reached is that the results obtained by applying a room division present a significant error reduction. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & & & & & Room & \\ \cline{2-7}  & **Stairs** & **Toilet** & **Corridor** & **Printer Area** & **Office** & **Average** \\ \hline
**Average error X (m)** & 0.0738 & 0.0823 & 0.8753 & 0.1466 & 0.1828 & 0.4523 \\ \hline
**Average error Y (m)** & 0.1033 & 0.0814 & 0.0902 & 0.2245 & 0.328 & 0.1452 \\ \hline
**Maximum error X (m)** & 0.4254 & 0.4999 & 7.1901 & 0.4312 & 0.3991 & 7.1901 \\ \hline
**Minimum error X (m)** & 0.0014 & 0.0005 & 0.00014 & 0.0012 & 0.0026 & 0.0001 \\ \hline
**Maximum error Y (m)** & 0.755 & 0.4061 & 0.5747 & 0.652 & 0.8784 & 0.8784 \\ \hline
**Minimum error Y (m)** & 0.0003 & 0.0001 & \(5.52\times 10^{-5}\) & 0.0008 & \(9.78\times 10^{-5}\) & \(5.53\times 10^{-5}\) \\ \hline
**Maximum error Euclidean distance (m)** & 0.7557 & 0.5207 & 7.1907 & 0.6606 & 0.9152 & 7.1907 \\ \hline
**Minimum error Euclidean distance (m)** & 0.00768 & 0.006 & 0.0169 & 0.0342 & 0.0569 & 0.006 \\ \hline
**Average error Euclidean distance (m)** & 0.1398 & 1.313 & 8.9920 & 2.9280 & 0.4011 & 0.5315 \\ \hline
**Error deviation in X (m)** & 0.0678 & 0.0852 & 1.2302 & 0.1081 & 0.1034 & 0.8979 \\ \hline
**Error deviation in Y (m)** & 0.1339 & 0.0813 & 0.0944 & 0.1514 & 0.2181 & 0.1586 \\ \hline
**Error deviation Euclidean distance (m)** & 0.1382 & 0.1005 & 1.2199 & 0.144 & 0.1961 & 0.8801 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Localization results performed by the regression CNNs. Results (errors in the estimation and deviations, as detailed in the left column) are presented separately for coordinate X and coordinate Y and globally for both coordinates (Euclidean distance). Concerning the corridor room, the main errors were observed along the \(X\) axis. Hence, in this case, similar to the solution presented for the office room, an automatic split of the room is addressed by means of a spectral clustering method. Due to the length of this room, it was split into five areas. Figure 5: Global representation of the coordinates. The orange points represent the ground truth and the blue points are the estimations made by the regression CNNs from each test image. Figure 6: Results for the office pose estimation reached by applying the proposed method to improve the accuracy of the regression CNN. After carrying out the division and training of the regression CNNs (one per area), the pose estimation is performed in the corridor room.