Cartographie dense basée sur une représentation compacte RGB-D dédiée à la navigation autonome

Our aim is concentrated around building ego-centric topometric maps represented as a graph of keyframe nodes which can be efficiently used by autonomous agents. The keyframe nodes which combines a spherical image and a depth map (augmented visual sphere) synthesises information collected in a local area of space by an embedded acquisition system. The representation of the global environment consists of a collection of augmented visual spheres that provide the necessary coverage of an operational area. A "pose" graph that links these spheres together in six degrees of freedom, also defines the domain potentially exploitable for navigation tasks in real time. As part of this research, an approach to map-based representation has been proposed by considering the following issues : how to robustly apply visual odometry by making the most of both photometric and ; geometric information available from our augmented spherical database ; how to determine the quantity and optimal placement of these augmented spheres to cover an environment completely ; how tomodel sensor uncertainties and update the dense infomation of the augmented spheres ; how to compactly represent the information contained in the augmented sphere to ensure robustness, accuracy and stability along an explored trajectory by making use of saliency maps.Dans ce travail, nous proposons une représentation efficace de l’environnement adaptée à la problématique de la navigation autonome. Cette représentation topométrique est constituée d’un graphe de sphères de vision augmentées d’informations de profondeur. Localement la sphère de vision augmentée constitue une représentation égocentrée complète de l’environnement proche. Le graphe de sphères permet de couvrir un environnement de grande taille et d’en assurer la représentation. Les "poses" à 6 degrés de liberté calculées entre sphères sont facilement exploitables par des tâches de navigation en temps réel. Dans cette thèse, les problématiques suivantes ont été considérées : Comment intégrer des informations géométriques et photométriques dans une approche d’odométrie visuelle robuste ; comment déterminer le nombre et le placement des sphères augmentées pour représenter un environnement de façon complète ; comment modéliser les incertitudes pour fusionner les observations dans le but d’augmenter la précision de la représentation ; comment utiliser des cartes de saillances pour augmenter la précision et la stabilité du processus d’odométrie visuelle

A Novel Inpainting Framework for Virtual View Synthesis

Multi-view imaging has stimulated significant research to enhance the user experience of free viewpoint video, allowing interactive navigation between views and the freedom to select a desired view to watch. This usually involves transmitting both textural and depth information captured from different viewpoints to the receiver, to enable the synthesis of an arbitrary view. In rendering these virtual views, perceptual holes can appear due to certain regions, hidden in the original view by a closer object, becoming visible in the virtual view. To provide a high quality experience these holes must be filled in a visually plausible way, in a process known as inpainting. This is challenging because the missing information is generally unknown and the hole-regions can be large. Recently depth-based inpainting techniques have been proposed to address this challenge and while these generally perform better than non-depth assisted methods, they are not very robust and can produce perceptual artefacts. This thesis presents a new inpainting framework that innovatively exploits depth and textural self-similarity characteristics to construct subjectively enhanced virtual viewpoints. The framework makes three significant contributions to the field: i) the exploitation of view information to jointly inpaint textural and depth hole regions; ii) the introduction of the novel concept of self-similarity characterisation which is combined with relevant depth information; and iii) an advanced self-similarity characterising scheme that automatically determines key spatial transform parameters for effective and flexible inpainting. The presented inpainting framework has been critically analysed and shown to provide superior performance both perceptually and numerically compared to existing techniques, especially in terms of lower visual artefacts. It provides a flexible robust framework to develop new inpainting strategies for the next generation of interactive multi-view technologies

ASTRAL PROJECTION: THEORIES OF METAPHOR, PHILOSOPHIES OF SCIENCE, AND THE ART O F SCIENTIFIC VISUALIZATION

This thesis provides an intellectual context for my work in computational scientific visualization for large-scale public outreach in venues such as digitaldome planetarium shows and high-definition public television documentaries. In my associated practicum, a DVD that provides video excerpts, 1 focus especially on work I have created with my Advanced Visualization Laboratory team at the National Center for Supercomputing Applications (Champaign, Illinois) from 2002-2007. 1 make three main contributions to knowledge within the field of computational scientific visualization. Firstly, I share the unique process 1 have pioneered for collaboratively producing and exhibiting this data-driven art when aimed at popular science education. The message of the art complements its means of production: Renaissance Team collaborations enact a cooperative paradigm of evolutionary sympathetic adaptation and co-creation. Secondly, 1 open up a positive, new space within computational scientific visualization's practice for artistic expression—especially in providing a theory of digi-epistemology that accounts for how this is possible given the limitations imposed by the demands of mapping numerical data and the computational models derived from them onto visual forms. I am concerned not only with liberating artists to enrich audience's aesthetic experiences of scientific visualization, to contribute their own vision, but also with conceiving of audiences as co-creators of the aesthetic significance of the work, to re-envision and re-circulate what they encounter there. Even more commonly than in the age of traditional media, on-line social computing and digital tools have empowered the public to capture and repurpose visual metaphors, circulating them within new contexts and telling new stories with them. Thirdly, I demonstrate the creative power of visaphors (see footnote, p. 1) to provide novel embodied experiences through my practicum as well as my thesis discussion. Specifically, I describe how the visaphors my Renaissance Teams and I create enrich the Environmentalist Story of Science, essentially promoting a counter-narrative to the Enlightenment Story of Science through articulating how humanity participates in an evolving universal consciousness through our embodied interaction and cooperative interdependence within nested, self-producing (autopoetic) systems, from the micro- to the macroscopic. This contemporary account of the natural world, its inter-related systems, and their dynamics may be understood as expressing a creative and generative energy—a kind of consciousness-that transcends the human yet also encompasses it

Towards adaptive and autonomous humanoid robots: from vision to actions

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions

Personal imaging

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts & Sciences, 1997.Includes bibliographical references (p. 217-223).In this thesis, I propose a new synergy between humans and computers, called "Humanistic Intelligence" (HI), and provide a precise definition of this new form of human-computer interaction. I then present a means and apparatus for reducing this principle to practice. The bulk of this thesis concentrates on a specific embodiment of this invention, called Personal Imaging, most notably, a system which I show attains new levels of creativity in photography, defines a new genre of documentary video, and goes beyond digital photography/video to define a new renaissance in imaging, based on simple principles of projective geometry combined with linearity and superposition properties of light. I first present a mathematical theory of imaging which allows the apparatus to measure, to within a single unknown constant, the quantity of light arriving from each direction, to a fixed point in space, using a collection of images taken from a sensor array having a possibly unknown nonlinearity. Within the context of personal imaging, this theory is a contribution in and of itself (in the sense that it was an unsolved problem previously), but when also combined with the proposed apparatus, it allows one to construct environment maps by simply looking around. I then present a new form of connected humanistic intelligence in which individuals can communicate, across boundaries of time and space, using shared environment maps, and the resulting computer-mediated reality that arises out of long-term adaptation in a personal imaging environment. Finally, I present a new philosophical framework for cultural criticism which arises out of a new concept called 'humanistic property'. This new philosophical framework has two axes, a 'reflectionist' axis and a 'diffusionist' axis. In particular, I apply the new framework to personal imaging, thus completing a body of work that lies at the intersection of art, science, and technology.by Steve Mann.Ph.D