# A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images
## 4 Data Base  
The COLD database [46] provides suitable datasets to evaluate localization algorithms, since sensor data were captured by a mobile robot under real operation conditions (people occluding partially the images, blur effect, etc.) and the structures led to visual aliasing, which is very usual in indoor environments. Moreover, these datasets also permit testing the influence of the algorithms under changes of illumination conditions.  
The images were captured both in rooms that present a repetitive structure and therefore, some visual similarity (such as corridor, toilets, etc.), and also in some other more specific rooms, which present visually distinctive characteristics. Furthermore, the Freiburg dataset presents two parts of the laboratories separately. Those parts are completely inde pendent and they are not related; hence, they can be considered as different environments. The present work was conducted by using the part A of the Freiburg dataset, since it is composed by five rooms that are present in every dataset (usual rooms). The trajectory studied is the path followed by the robot that visits 5 out the 9 rooms which compose the environment. The map of the proposed dataset is shown in [47] and it is entitled Part A. For this work, the trajectory studied was the one depicted with the blue dashed lines. Table 1 shows the abbreviation code, name and number of images for each evaluated room. As for appearance of the omnidirectional images, three examples are shown in Figure 1. These images were captured, respectively, under three different illumination conditions. In the present work, the omnidirectional images were used directly as they were obtained, i.e., they were not processed to obtain either a panoramic image nor a set of monocular images.  
Concerning the robot and the acquisition system in the Freiburg dataset, the images were captured by a robot equipped with a visual catadioptric system, a SICK laser sensor and encoders in the wheels. The visual system is composed of monocular standard images and omnidirectional images. The omnidirectional images capturing process is based on the use of a hyperbolic mirror. The images are captured with a frame rate of 5 images/s and the robot moving with an average speed of 0.3 m/s. The ground truth information is provided by the laser sensor. The dataset also provides a label per image, which indicates the room from which it was captured. In this sense, each image is labeled with a string array (code of the room). For example CR-A, PA-B, where CR and PA are the abbreviation code (corridor and printer area) and A or B determines the part of the laboratory. The labeling was transformed from a string array to one-hot vector, with the aim of using the information to carry out the training of the CNN for classification (in this case, room retrieval). Hence, the labeling transformation is arranged as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Code**} & \multirow{2}{*}{**Room Name**} & \multicolumn{3}{c}{**Number of Images**} \\ \cline{3-5}  & & **Cloudy** & **Sunny** & **Night** \\ \hline
1-PA & Printer area & 692 & 985 & 716 \\ \hline
2-CR & Corridor & 2472 & 3132 & 2537 \\ \hline
8-TL & Toilet & 683 & 854 & 645 \\ \hline
9-ST & Stairs area & 433 & 652 & 649 \\ \hline
5-2 PO1 & Office & 609 & 745 & 649 \\ \hline - & Total & 4889 & 6368 & 5196 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of images for each room and for each illumination condition.  
Figure 1: Sample omnidirectional images from the Freiburg dataset, part A. The images were captured under (**a**) cloudy, (**b**) night and (**c**) sunny illumination conditions.