Using a RGB-D camera for 6DoF SLAM

This paper presents a method for fast calculation of the egomotion done by a robot using visual features. The method is part of a complete system for automatic map building and Simultaneous Localization and Mapping (SLAM). The method uses optical flow in order to determine if the robot has done a movement. If so, some visual features which do not accomplish several criteria (like intersection, unicity, etc,) are deleted, and then the egomotion is calculated. We use a state-of-the-art algorithm (TORO) in order to rectify the map and solve the SLAM problem. The proposed method provides better efficiency that other current methods.These authors want to express their gratitude to Spanish Ministry of Science and Technology (MYCIT) and the Research and Innovation Vice-president Office of the University of Alicante for their financial support through the projects DPI2009-07144 and GRE10-16, respectively

CIBERER : Spanish national network for research on rare diseases: A highly productive collaborative initiative

Altres ajuts: Instituto de Salud Carlos III (ISCIII); Ministerio de Ciencia e Innovación.CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research

A study of 2D features for 3D visual SLAM

Paper submitted to the 43rd International Symposium on Robotics (ISR), Taipei, Taiwan, August 29-31, 2012.The use of 2D features in computer vision has had a great impact in lots of applications. For example, the combination of those features together with 3D data has helped to solve the Simultaneous Location And Mapping (SLAM) problem in real time. Nowadays, there are several interesting feature detectors and descriptors with different characteristics: processing time, robustness against lightning conditions, changes in point of view, scale, etc., and every day it appears more and more. In this paper, a deeper study about several of these detectors and descriptors is done. Several interesting graphs where we can separate distances with respect to axis and angles have been analysed. This study helps to make decisions about which are better for a given application. The study has been done with a low cost sensor as Kinect installed on robotics arm to control the movements with accuracy. Furthermore, finally, real scenario reconstruction is shown using Kinect camera and the visual features analysed in the study

Evaluation of the fat content in a small-calibre Salami made with pork from Chato Murciano breed Evaluation of the fat content in a small-calibre Salami made with pork from Chato Murciano breed

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An improvement of a SLAM RGB-D method with movement prediction derived from a study of visual features

This paper presents a method for the fast calculation of a robot’s egomotion using visual features. The method is part of a complete system for automatic map building and Simultaneous Location and Mapping (SLAM). The method uses optical flow to determine whether the robot has undergone a movement. If so, some visual features that do not satisfy several criteria are deleted, and then egomotion is calculated. Thus, the proposed method improves the efficiency of the whole process because not all the data is processed. We use a state-of-the-art algorithm (TORO) to rectify the map and solve the SLAM problem. Additionally, a study of different visual detectors and descriptors has been conducted to identify which of them are more suitable for the SLAM problem. Finally, a navigation method is described using the map obtained from the SLAM solution.The authors wish to express their gratitude to the Office of the Vice President for Research, Development and Innovation of the University of Alicante and the Valencia Regional Government for their financial support through the projects GRE10-16 and GV 2012/102, respectively