Classification of textures seen from different distances and under varying illumination direction

Changes in the angle of illumination incident upon a 3D surface texture can significantly alter its appearance, implying variations in the image texture. These texture variations produce displacements of class members in the feature space, increasing the failure rates of texture classifiers. To avoid this problem, a model-based texture recognition system which classifies textures seen from different distances and under different illumination directions is presented in this paper. The system works on the basis of a surface model obtained by means of 4-source colour photometric stereo, used to generate 2D image textures under different illumination directions. The recognition system combines coocurrence matrices for feature extraction with a Nearest Neighbour classifier. Moreover, the recognition allows one to guess the approximate direction of the illumination used to capture the test image

Underwater SLAM in a marina environment

This paper describes a navigation system for autonomous underwater vehicles (AUVs) in partially structured environments, such as dams, harbors, marinas or marine platforms. A mechanical scanning imaging sonar is used to obtain information about the location of planar structures present in such environments. A modified version of the Hough transform has been developed to extract line features, together with their uncertainty, from the continuous sonar dataflow. The information obtained is incorporated into a feature-based SLAM algorithm running an Extended Kalman Filter (EKF). Simultaneously, the AUV's position estimate is provided to the feature extraction algorithm to correct the distortions that the vehicle motion produces in the acoustic images. Experiments carried out in a marina located in the Costa Brava (Spain) with the Ictineu AUV show the viability of the proposed approach

Selective submap joining SLAM for autonomous vehicles

Simultaneous Localization and Mapping (SLAM) do not result in consistent maps of large areas because of gradual increase of the uncertainty for long term missions. In addition, as the size of the map grows the computational cost increases, making SLAM solutions unsuitable for on-line applications. This thesis surveys SLAM approaches paying special attention to those approaches aimed to work on large scenarios. Special focus is given to existing underwater SLAM applications. A technique based on using independent local maps together with a global stochastic map is presented. This technique is called Selective Submap Joining SLAM (SSJS). A global map contains relative transformations between local maps, which are updated once a new loop is detected. Maps sharing several features are fused, maintaining the correlation between landmarks and vehicle's pose. The use of local maps reduces computational costs and improves map consistency as compared to state of the art techniques.

Capaware: plataforma de desarrollo de software libre para aplicacines geográficas 3D multicapa

En este trabajo se presenta Capaware, una plataforma de software libre para el desarrollo de aplicaciones geográficas 3D multicapa, que surge a partir de la iniciativa del Instituto Tecnológico de Canarias en colaboración con la Universidad de Las Palmas de Gran Canaria. Este entorno simplifica la creación de aplicaciones 3D sobre territorios geográficos extensos, disponiendo de una herramienta muy visual que aporta un nuevo punto de vista muy importante para una toma de decisiones eficaz. Capaware proporciona una interfaz fácil de usar y muy flexible que simplifica el desarrollo de nuevas aplicaciones, permitiéndonos crear rápidamente entornos virtuales con múltiples capas de información sobre el terreno. Con las capacidades clásicas de un Sistema de Información Geográfica (SIG), Capaware permite actualmente la carga de capas WMS sobre entornos 3D, añadir objetos 3D sobre el terreno, y visualizar elementos dinámicos, ofreciendo una nueva perspectiva de la información analizada. Así mismo, podemos administrar las capas de recursos y elementos que se pueden representar sobre la zona geográfica en cuestión. (...)

Online Robust 3D Mapping Using Structure from Motion Cues

This paper presents a complete solution for creating accurate 3D textured models from monocular video sequences. The methods are developed within the framework of sequential structure from motion, where a 3D model of the environment is maintained and updated as new visual information becomes available. The camera position is recovered by directly associating the 3D scene model with local image observations. Compared to standard structure from motion techniques, this approach decreases the error accumulation while increasing the robustness to scene occlusions and feature association failures. The obtained 3D information is used to generate high quality, composite visual maps of the scene (mosaics). The visual maps are used to create texture-mapped, realistic views of the scene