Construcción de mapas 3D y extracción de primitivas geométricas del entorno

Este trabajo se centra en la construcción de mapas 3D a partir de datos de rango obtenidos mediante una cámara estéreo. El proceso de reconstrucción lo hemos dividido en dos fases. En la primera, a partir de datos tomados a intervalos regulares por un robot dentro de un entorno, hemos solucionado parcialmente el problema del error de odometría, haciendo uso de un método novedoso de emparejamiento 3D. En la segunda y haciendo uso del resultado de la fase anterior, nos planteamos extraer primitivas geométricas (planos, cilindros, etc.) para realizar una reconstrucción del entorno mediante estas primitivas.Secretaría de Estado de Educación y Universidade

A robust and fast method for 6DoF motion estimation from generalized 3D data

Nowadays, there is an increasing number of robotic applications that need to act in real three-dimensional (3D) scenarios. In this paper we present a new mobile robotics orientated 3D registration method that improves previous Iterative Closest Points based solutions both in speed and accuracy. As an initial step, we perform a low cost computational method to obtain descriptions for 3D scenes planar surfaces. Then, from these descriptions we apply a force system in order to compute accurately and efficiently a six degrees of freedom egomotion. We describe the basis of our approach and demonstrate its validity with several experiments using different kinds of 3D sensors and different 3D real environments.This work has been supported by project DPI2009-07144 from Ministerio de Educación y Ciencia (Spain) and GRE10-35 from Universidad de Alicante (Spain)

JavaVis: An Integrated Computer Vision Library for Teaching Computer Vision

In this article, we present a new framework oriented to teach Computer Vision related subjects called JavaVis. It is a computer vision library divided in three main areas: 2D package is featured for classical computer vision processing; 3D package, which includes a complete 3D geometric toolset, is used for 3D vision computing; Desktop package comprises a tool for graphic designing and testing of new algorithms. JavaVis is designed to be easy to use, both for launching and testing existing algorithms and for developing new ones.This work was supported by project GRE10‐35 from Universidad de Alicante (Spain) and grant GITE‐09017‐UA of University of Alicante

Experiences Using an Open Source Sofware Library to Teach Computer Vision Subjects

Machine vision is an important subject in computer science and engineering degrees. For laboratory experimentation, it is desirable to have a complete and easy-to-use tool. In this work we present a Java library, oriented to teaching computer vision. We have designed and built the library from the scratch with enfasis on readability and understanding rather than on efficiency. However, the library can also be used for research purposes. JavaVis is an open source Java library, oriented to the teaching of Computer Vision. It consists of a framework with several features that meet its demands. It has been designed to be easy to use: the user does not have to deal with internal structures or graphical interface, and should the student need to add a new algorithm it can be done simply enough. Once we sketch the library, we focus on the experience the student gets using this library in several computer vision courses. Our main goal is to find out whether the students understand what they are doing, that is, find out how much the library helps the student in grasping the basic concepts of computer vision. In the last four years we have conducted surveys to assess how much the students have improved their skills by using this library

Compression and registration of 3D point clouds using GMMs

3D data sensors provide an enormous amount of information. It is necessary to develop efficient methods to manage this information under certain time, bandwidth or storage space requirements. In this work, we propose a 3D compression and decompression method. This method also allows the use of the compressed data for a registration process. First, points are selected and grouped, using a 3D-model based on planar surfaces. Next, we use a fast variant of Gaussian Mixture Models and an Expectation-Maximization algorithm to replace the points grouped in the previous step with a set of Gaussian distributions. These learned models can be used as features to find matches between two consecutive poses and apply 3D pose registration using RANSAC. Finally, the 3D map can be obtained by decompressing the models.This work has been supported by the Spanish Government TIN2016-76515-R Grant, supported with Feder funds

Combining visual features and Growing Neural Gas networks for robotic 3D SLAM

The use of 3D data in mobile robotics provides valuable information about the robot’s environment. Traditionally, stereo cameras have been used as a low-cost 3D sensor. However, the lack of precision and texture for some surfaces suggests that the use of other 3D sensors could be more suitable. In this work, we examine the use of two sensors: an infrared SR4000 and a Kinect camera. We use a combination of 3D data obtained by these cameras, along with features obtained from 2D images acquired from these cameras, using a Growing Neural Gas (GNG) network applied to the 3D data. The goal is to obtain a robust egomotion technique. The GNG network is used to reduce the camera error. To calculate the egomotion, we test two methods for 3D registration. One is based on an iterative closest points algorithm, and the other employs random sample consensus. Finally, a simultaneous localization and mapping method is applied to the complete sequence to reduce the global error. The error from each sensor and the mapping results from the proposed method are examined.This work has been supported by Grant DPI2009-07144 and DPI2013-40534-R from Ministerio de Ciencia e Innovacion of the Spanish Government, University of Alicante Projects GRE09-16 and GRE10-35, and Valencian Government Project GV/2011/034

Color smoothing for RGB-D data using entropy information

RGB-D sensors are capable of providing 3D points (depth) together with color information associated with each point. These sensors suffer from different sources of noise. With some kinds of RGB-D sensors, it is possible to pre-process the color image before assigning the color information to the 3D data. However, with other kinds of sensors that is not possible: RGB-D data must be processed directly. In this paper, we compare different approaches for noise and artifacts reduction: Gaussian, mean and bilateral filter. These methods are time consuming when managing 3D data, which can be a problem with several real time applications. We propose new methods to accelerate the whole process and improve the quality of the color information using entropy information. Entropy provides a framework for speeding up the involved methods allowing certain data not to be processed if the entropy value of that data is over or under a given threshold. The experimental results provide a way to balance the quality and the acceleration of these methods. The current results show that our methods improve both the image quality and processing time, as compared to the original methods.This work has been partially supported by grant DPI2013-40534-R from Ministerio de Economia y competitividad of the Spanish Government, supported by Feder funds, and Valencian’s Government project GV/2014/097

Evaluación continua utilizando controles on-line. Percepción de los estudiantes

En la asignatura Sistemas Inteligentes del grado en Ingeniería Informática, durante el curso 2013-14, se planteó a los estudiantes un sistema de evaluación continua de la parte teórica consistente en la realización de controles on-line utilizando cuestionarios Moodle. En las normas de evaluación de la asignatura, los controles acumulaban toda la materia impartida hasta la fecha y su peso creciente de cara a la obtención de la nota final. Teníamos especial interés en determinar si el sistema resultaba estimulante para los estudiantes. El artículo aborda la evaluación continua, el contexto de la asignatura y los criterios de evaluación que se establecieron para la evaluación de la parte teórica de la asignatura. Además, se presentan las características de los cuestionarios Moodle, se aporta la encuesta de valoración del método que se pasó a los estudiantes, los resultados obtenidos en la citada encuesta, las conclusiones y las recomendaciones para el futuro. Deseamos destacar el soporte de la “Red de Investigación en Sistemas Inteligentes. Evaluación continua mediante controles utilizando Moodle” Código de Red ICE: 3021

Red de Investigación en Sistemas Inteligentes. Evaluación continua mediante controles utilizando Moodle

Esta memoria corresponde al trabajo desarrollado durante el curso 2013-2014 por los componentes de la “Red de Investigación en Sistemas Inteligentes. Evaluación continua mediante controles utilizando Moodle”. Código de Red ICE: 3021. En la asignatura Sistemas Inteligentes del grado en Ingeniería Informática, durante el curso 2013-14, se planteó a los estudiantes un sistema de evaluación continua de la parte teórica consistente en la realización de controles on-line utilizando cuestionarios Moodle. En las normas de evaluación de la asignatura, los controles acumulaban toda la materia impartida hasta la fecha y su peso creciente de cara a la obtención de la nota final. Teníamos especial interés en determinar si el sistema resultaba estimulante para los estudiantes debido a considerar que este aspecto es fundamental en el proceso de aprendizaje. La memoria se organiza de la siguiente forma: se inicia analizando la viabilidad de la evaluación continua y su interés pedagógico en nuestro contexto, seguidamente se presenta la metodología empleada, las características de los cuestionarios Moodle, se aportan los estudios que nos han permitido validar nuestra propuesta, las conclusiones y las recomendaciones que permitirán mejorar los procesos y resultados en el futuro

Una concepción moderna de Técnicas de Inteligencia Artificial en la Universidad de Alicante

Este trabajo tiene como objetivo presentar la estructura de la asignatura Técnicas de Inteligencia Artificial en la Universidad de Alicante haciendo especial hincapié en los recursos (nuevas tecnologías, material docente y aplicaciones software) empleados durante el desarrollo de la misma. Parte de estos recursos docentes están siendo empleados como herramienta de apoyo para la adaptación al Espacio Europeo de Educación Superior