# A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images
## 1 Introduction  
Localization is an essential ability for a mobile robot to be autonomous. In order to tackle high level tasks, a mobile robot must be able to create a map of the environment, localize itself in this map and perform a path planing strategy in this environment. To explore the environment the robot must be provided with a sensor system that captures information around it. There exists extensive research related to sensors used with mobile robots, such as SONAR, laser, GPS or cameras [1, 2, 3, 4]. Among them, vision systems have significantly attracted the attention of the scientific community [5, 6, 7]. Compared to other kinds of systems, cameras are relatively cheap devices and they are capable of extracting a high amount of information from the environment. This work is based on the use of omnidirectional cameras due to their wide field of view since them provide in one single image, 360 deg information around the robot. These sensors along with others already mentioned such as SONAR or lasers can provide a precise, robust and economic solution to localization when combined with current Artificial Intelligence (AI)-based visual recognition technologies [8, 9], which constitute another growing sector. The efficiency of omnidirectional images in mapping and localization tasks depends basically on how the visual information is described. Many description methods have been used in mapping tasks carried out by mobile robots [10, 11]. Some of these methods extract characteristic points of the environment and an associated descriptor containing certain information that provides invariance to many changes such as lighting, point of view and other transformations. Some of the most used methods are descriptors based on local features such as the scale-invariant feature transform (SIFT) [12], which extracts and describes characteristic points invariant to rotation, scale and change of lighting conditions. Speeded up robust features (SURFs) [13] is based on SIFT points but with more robustness to translation changes and less computational cost. Other methods are based on global descriptors [14]. The descriptor encodes the information of the whole image instead of only local information. This is the case of Principal Components Analysis (PCA) [15] and methods based on Deep Learning techniques [16; 17]. Deep learning is a branch of the artificial intelligence (AI) that in recent years has experienced great improvements due to its potential and hardware development. Deep learning includes tools such as Convolutional Neural Networks (CNNs) that, despite their often computationally expensive training process, have shown excellent results in image classification tasks and recognition [9; 16; 17; 18]. These algorithms allow applying filters in order to extract global descriptors of the input image. Broadly speaking, the architecture of a CNN consists of an input layer, hidden layers and an output. However, different CNNs architectures can be found in the literature according to the number and typology of the hidden layers and the way they are connected. The selection of the most suitable CNN architecture depends on the task to be performed. For example, some of them such as GoogleNet, ResNetm, VGG or AlexNet are remarkable according to their excellent results in classification [14; 19; 20; 21]. Omnidirectional images in visual navigation are in widespread use [22; 23; 24; 25]. For example, [PERSON] et al. [22] propose the use of omnidirectional images to find a solution for the localization of a mobile robot. In particular, they use the panorama obtained from the captured image and perform a correlation method that is then refined using a Kalman filter by incorporating the dynamic information of the robot model. Furthermore, [PERSON] and [PERSON] [23] present a technique for localization of mobile robots based on image feature matching from omnidirectional vision.