6 research outputs found
Percepci贸n basada en visi贸n estereosc贸pica, planificaci贸n de trayectorias y estrategias de navegaci贸n para exploraci贸n rob贸tica aut贸noma
Tesis in茅dita de la Universidad Complutense de Madrid, Facultad de Inform谩tica, Departamento de Ingenier铆a del Software e Inteligencia artificial, le铆da el 13-05-2015En esta tesis se trata el desarrollo de una estrategia de navegaci贸n aut贸noma basada en visi贸n artificial para exploraci贸n rob贸tica aut贸noma de superficies planetarias. Se han desarrollado una serie de subsistemas, m贸dulos y software espec铆ficos para la investigaci贸n desarrollada en este trabajo, ya que la mayor铆a de las herramientas existentes para este dominio son propiedad de agencias espaciales nacionales, no accesibles a la comunidad cient铆fica. Se ha dise帽ado una arquitectura software modular multi-capa con varios niveles jer谩rquicos para albergar el conjunto de algoritmos que implementan la estrategia de navegaci贸n aut贸noma y garantizar la portabilidad del software, su reutilizaci贸n e independencia del hardware. Se incluye tambi茅n el dise帽o de un entorno de trabajo destinado a dar soporte al desarrollo de las estrategias de navegaci贸n. 脡ste se basa parcialmente en herramientas de c贸digo abierto al alcance de cualquier investigador o instituci贸n, con las necesarias adaptaciones y extensiones, e incluye capacidades de simulaci贸n 3D, modelos de veh铆culos rob贸ticos, sensores, y entornos operacionales, emulando superficies planetarias como Marte, para el an谩lisis y validaci贸n a nivel funcional de las estrategias de navegaci贸n desarrolladas. Este entorno tambi茅n ofrece capacidades de depuraci贸n y monitorizaci贸n.La presente tesis se compone de dos partes principales. En la primera se aborda el dise帽o y desarrollo de las capacidades de autonom铆a de alto nivel de un rover, centr谩ndose en la navegaci贸n aut贸noma, con el soporte de las capacidades de simulaci贸n y monitorizaci贸n del entorno de trabajo previo. Se han llevado a cabo un conjunto de experimentos de campo, con un robot y hardware real, detall谩ndose resultados, tiempo de procesamiento de algoritmos, as铆 como el comportamiento y rendimiento del sistema en general. Como resultado, se ha identificado al sistema de percepci贸n como un componente crucial dentro de la estrategia de navegaci贸n y, por tanto, el foco principal de potenciales optimizaciones y mejoras del sistema. Como consecuencia, en la segunda parte de este trabajo, se afronta el problema de la correspondencia en im谩genes est茅reo y reconstrucci贸n 3D de entornos naturales no estructurados. Se han analizado una serie de algoritmos de correspondencia, procesos de imagen y filtros. Generalmente se asume que las intensidades de puntos correspondientes en im谩genes del mismo par est茅reo es la misma. Sin embargo, se ha comprobado que esta suposici贸n es a menudo falsa, a pesar de que ambas se adquieren con un sistema de visi贸n compuesto de dos c谩maras id茅nticas. En consecuencia, se propone un sistema experto para la correcci贸n autom谩tica de intensidades en pares de im谩genes est茅reo y reconstrucci贸n 3D del entorno basado en procesos de imagen no aplicados hasta ahora en el campo de la visi贸n est茅reo. 脡stos son el filtrado homom贸rfico y la correspondencia de histogramas, que han sido dise帽ados para corregir intensidades coordinadamente, ajustando una imagen en funci贸n de la otra. Los resultados se han podido optimizar adicionalmente gracias al dise帽o de un proceso de agrupaci贸n basado en el principio de continuidad espacial para eliminar falsos positivos y correspondencias err贸neas. Se han estudiado los efectos de la aplicaci贸n de dichos filtros, en etapas previas y posteriores al proceso de correspondencia, con eficiencia verificada favorablemente. Su aplicaci贸n ha permitido la obtenci贸n de un mayor n煤mero de correspondencias v谩lidas en comparaci贸n con los resultados obtenidos sin la aplicaci贸n de los mismos, consiguiendo mejoras significativas en los mapas de disparidad y, por lo tanto, en los procesos globales de percepci贸n y reconstrucci贸n 3D.Depto. de Ingenier铆a de Software e Inteligencia Artificial (ISIA)Fac. de Inform谩ticaTRUEunpu
Machine Learning techniques applied to stereo vision
Stereo is a popular technique enabling fast and dense depth estimation from two or more images.
Its success is mainly due to its easiness of deployment, requiring only a couple or multiple synchronized image sensors, accurately calibrated to solve the matching problem between pixels on one of the images (named reference) and the other (named target). The absence of active technologies (e.g. pattern projection, laser scanners etc..) make this solution deployable on almost every scenario. Despite the wide literature concerning stereo, it still represents an open problem because of very challenging conditions such as poor illumination, reflective surfaces, occlusions and other elements occurring in real environments.
Two main trends in stereo vision acquired popularity in the last years: confidence estimation and machine learning. Both proved to be very effective, pushing forward the state-of-the-art of dense disparity estimation.
In this thesis, we combine these two trends to improve both confidence estimation and disparity inference, by defining more effective and easier to deploy confidence measures and proposing new approaches to leverage on them for more accurate depth prediction.
All the experiments are validated on three popular datasets, KITTI 2012, KITTI 2015 and Middlebury v3, following the commonly adopted methodologies and protocol to compare our proposals with previous works representing the state-of-the-art in stereo vision
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Camera positioning for 3D panoramic image rendering
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Virtual camera realisation and the proposition of trapezoidal camera architecture are the two broad contributions of this thesis. Firstly, multiple camera and their arrangement constitute a critical component which affect the integrity of visual content acquisition for multi-view video. Currently, linear, convergence, and divergence arrays are the prominent camera topologies adopted. However, the large number of cameras required and their synchronisation are two of prominent challenges usually encountered. The use of virtual cameras can significantly reduce the number of physical cameras used with respect to any of the known
camera structures, hence adequately reducing some of the other implementation issues. This thesis explores to use image-based rendering with and without geometry in the implementations leading to the realisation of virtual cameras. The virtual camera implementation was carried out from the perspective of depth map (geometry) and use of multiple image samples (no geometry). Prior to the virtual camera realisation, the generation of depth map was investigated using region match measures widely known for solving image point correspondence problem. The constructed depth maps have been compare with the ones generated
using the dynamic programming approach. In both the geometry and no geometry approaches, the virtual cameras lead to the rendering of views from a textured depth map, construction of 3D panoramic image of a scene by stitching multiple image samples and performing superposition on them, and computation
of virtual scene from a stereo pair of panoramic images. The quality of these rendered images were assessed through the use of either objective or subjective analysis in Imatest software. Further more, metric reconstruction of a scene was performed by re-projection of the pixel points from multiple image samples with
a single centre of projection. This was done using sparse bundle adjustment algorithm. The statistical summary obtained after the application of this algorithm provides a gauge for the efficiency of the optimisation step. The optimised data was then visualised in Meshlab software environment, hence providing the reconstructed scene. Secondly, with any of the well-established camera arrangements, all cameras are usually constrained to the same horizontal plane. Therefore, occlusion becomes an extremely challenging problem, and a robust camera set-up is required in order to resolve strongly the hidden part of any scene objects.
To adequately meet the visibility condition for scene objects and given that occlusion of the same scene objects can occur, a multi-plane camera structure is highly desirable. Therefore, this thesis also explore trapezoidal camera structure for image acquisition. The approach here is to assess the feasibility and potential
of several physical cameras of the same model being sparsely arranged on the edge of an efficient trapezoid graph. This is implemented both Matlab and Maya. The quality of the depth maps rendered in Matlab are better in Quality