Un enfoque tolerante a interrupciones para la seguridad de la Internet de las cosas

La Internet de las cosas (IoT, Internet of Things) es un paradigma emergente que pretende la interconexión de cualquier objeto susceptible de contar con una parte de electrónica, favorecido por la miniaturización de los componentes. El estado de desarrollo de la IoT hace que no haya ninguna propuesta firme para garantizar la seguridad y la comunicación extremo a extremo. En este artículo presentamos un trabajo en progreso hacia una aproximación tolerante a retrasos (DTN, Delay and Disruption Tolerant Networks) para la comunicación en el paradigma de la IoT y planteamos la adaptación de los mecanismo de seguridad existentes en DTN a la IoT.

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.

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.