Abstract This work presents a framework to create a visual model of the environment which can be used to estimate the position of a mobile robot by means of artificial intelligence techniques. The proposed framework retrieves the structure of the environment from a dataset composed of omnidirectional images captured along it. These images are described by means of global-appearance approaches. The information is arranged in two layers, with different levels of granularity. The first layer is obtained by means of classifiers and the second layer is composed of a set of data fitting neural networks. Subsequently, the model is used to estimate the position of the robot, in a hierarchical fashion, by comparing the image captured from the unknown position with the information in the model. Throughout this work, five classifiers are evaluated (Naïve Bayes, SVM, random forest, linear discriminant classifier and a classifier based on a shallow neural network) along with three different global-appearance descriptors (HOG, gist, and a descriptor calculated from an intermediate layer of a pre-trained CNN). The experiments have been tackled with some publicly available datasets of omnidirectional images captured indoors with the presence of dynamic changes. Several parameters are used to assess the efficiency of the proposal: the ability of the algorithm to estimate coarsely the position (hit ratio), the average error (cm) and the necessary computing time. The results prove the efficiency of the framework to model the environment and localize the robot from the knowledge extracted from a set of omnidirectional images with the proposed artificial intelligence techniques.

Nowadays education programs in engineering degrees have evolved towards advanced learning models and methodologies, which are either partially or entirely sustained by ICT (Information, Communication, and Technology) resources, and blended approaches. In this sense, electronics courses have become of paramount importance in most education plans within engineering degrees at university. Therefore the adaption to such novel methodologies is increasingly demanded. According to this, we propose an improved teaching program concentrated on the use of an online simulation tool, amongst other digital resources. The program is addressed to students in first levels of engineering degrees, within the framework of the Spanish public university system. In particular, the methodology has been devised through the use of an online circuit simulation applet in Java, which does not require any software installation. The main purpose is to enhance the general achievement of the students, particularizing on their practical competences, digital skills, engagement and motivation towards the learning of electronics, sustained by digital resources such as simulation. A population of 258 students enrolled during the academic year 2017/2018 has been established as a sample for presenting achievement results, surveys data and comparison statistics with other digital resources. Additionally, test groups of roughly 50% out of the total population of students have been established in order to confirm the success of the approach, in contrast to the former teaching methodology. As a result, the approach proves to be an active model which allows the students to develop long-term and autonomous skills in electronics and simulation.

In this work, a clustering approach to obtain compact topological models of an environment is developed and evaluated. The usefulness of these models is tested by studying their utility to solve the robot localization problem subsequently. Omnidirectional visual information and global appearance descriptors are used both to create and compress the models and to estimate the position of the robot. Comparing to the methods based on the extraction and description of landmarks, global appearance approaches permit building models that can be handled and interpreted more intuitively and using relatively straightforward algorithms to estimate the position of the robot. The proposed algorithms are tested with a set of panoramic images captured with a catadioptric vision sensor in a large environment under real working conditions. The results show that it is possible to compress substantially the visual information contained in topological models to arrive to a balance between the computational cost and the accuracy of the localization process.