Les environnements 3D immersifs et collaboratifs en ligne sont en pleine émergence. Ils posent les problématiques du sentiment de présence au sein des mondes virtuels, de l'immersion et des capacités d'interaction. Les systèmes 3D multi-caméra permettent, sur la base d'une information photométrique, d'extraire une information géométrique (modèle 3D) de la scène observée. Il est alors possible de calculer un modèle numérique texturé en temps-réel qui est utilisé pour assurer la présence de l'utilisateur dans l'espace numérique. Dans le cadre de cette thèse nous avons étudié comment coupler la capacité de présence fournie par un tel système avec une immersion visuelle et des interactions co-localisées. Ceci a mené à la réalisation d'une application qui couple un visio-casque, un système de suivi optique et un système multi-caméra. Ainsi l'utilisateur peut visualiser son modèle 3D correctement aligné avec son corps et mixé avec les objets virtuels. Nous avons aussi mis en place une expérimentation de télépresence sur 3 sites (Bordeaux, Grenoble, Orléans) qui permet à plusieurs utilisateurs de se rencontrer en 3D et de collaborer à distance. Le modèle 3D texturé donne une très forte impression de présence de l'autre et renforce les interactions physiques grâce au langage corporel et aux expressions faciales. Enfin, nous avons étudié comment extraire une information de vitesse à partir des informations issues des caméras, grâce au flot optique et à des correspondances 2D et 3D, nous pouvons estimer le déplacement dense du modèle 3D. Cette donnée étend les capacités d'interaction en enrichissant le modèle 3D. / Online immersive and collaborative 3D environments are emerging very fast. They raise the issues of sensation of presence within virtual worlds, immersion and interaction capabilities. Multi-camera 3D systems allow to extract geometrical information (3D model) of the observed scene using the photometric information. It enables calculation of a numerical textured model in real-time, which is then used to ensure the user's presence in cyberspace. In this thesis we have studied how to pair the ability of presence, obtained from such a system, with visual immersion and co-located interactions. This led to the realization of an application that combines a head mounted display, an optical tracking system and a multi-camera system. Thus, the user can view his 3D model correctly aligned with his own body and mixed with virtual objects. We also have implemented an experimental telepresence application featuring three sites (Bordeaux, Grenoble, Orleans) that allows multiple users to meet in 3D and collaborate remotely. Textured 3D model gives a very strong sense of presence of each other and strengthens the physical interactions, thanks to body language and facial expressions. Finally, we studied how to extract 3D velocity information from the cameras images; using 2D optical flow and 2D and 3D correspondences, we can estimate the dense displacement of the 3D model. This data extend the interaction capabilities by enriching the 3D model.

En raison de l'augmentation du volume et de la complexité des systèmes de transport, de nouveaux systèmes avancés d'assistance à la conduite (ADAS) sont étudiés dans de nombreuses entreprises, laboratoires et universités. Ces systèmes comprennent des algorithmes avec des techniques qui ont été étudiés au cours des dernières décennies, comme la localisation et cartographie simultanées (SLAM), détection d'obstacles, la vision stéréoscopique, etc. Grâce aux progrès de l'électronique, de la robotique et de plusieurs autres domaines, de nouveaux systèmes embarqués sont développés pour garantir la sécurité des utilisateurs de ces systèmes critiques. Pour la plupart de ces systèmes, une faible consommation d'énergie ainsi qu'une taille réduite sont nécessaires. Cela crée la contrainte d'exécuter les algorithmes sur les systèmes embarqués avec des ressources limitées. Dans la plupart des algorithmes, en particulier pour la vision par ordinateur, une grande quantité de données doivent être traitées à des fréquences élevées, ce qui exige des ressources informatiques importantes. Un FPGA satisfait cette exigence, son architecture parallèle combinée à sa faible consommation d'énergie et la souplesse pour les programmer permet de développer et d'exécuter des algorithmes plus efficacement que sur d'autres plateformes de traitement. Les composants virtuels développés dans cette thèse ont été utilisés dans trois différents projets: PICASSO (vision stéréoscopique), COMMROB (détection d'obstacles à partir d'une système multicaméra) et SART (Système d'Aide au Roulage tous Temps). / Due to the increase of traffic volume and complexity of new transport systems, new Advanced Driver Assistance Systems (ADAS) are a subject of research of many companies, laboratories and universities. These systems include algorithms with techniques that have been studied during the last decades like Simultaneous Lo- calization and Mapping (SLAM), obstacle detection, stereo vision, etc. Thanks to the advances in electronics, robotics and other domains, new embedded systems are being developed to guarantee the safety of the users of these critical systems. For most of these systems a low power consumption as well as reduced size is required. It creates the constraint of execute the algorithms in embedded devices with limited resources. In most of algorithms, moreover for computer vision ones, a big amount of data must be processed at high frequencies, this amount of data demands strong computing resources. FPGAs satisfy this requirement; its parallel architecture combined with its low power consumption and exibility allows developing and executing some algorithms more efficiently than any other processing platforms. In this thesis different embedded computer vision architectures intended to be used in ADAS using FPGAs are presented such as: We present the implementation of a distortion correction architecture operating at 100 Hz in two cameras simultaneously. The correction module allows also to rectify two images for implementation of stereo vision. Obstacle detection algorithms based on Inverse Perspective Mapping (IPM) and classiffication based on Color/Texture attributes are presented. The IPM transform is based in the perspective effect of a scene perceived from two different points of view. Moreover results of the detection algorithms from color/texture attributes applied on a multi-cameras system, are fused in an occupancy grid. An accelerator to apply homographies on images, is presented; this accelerator can be used for different applications like the generation of Bird's eye view or Side view. Multispectral vision is studied using both infrared images and color ones. Syn- thetic images are generated from information acquired from visible and infrared sources to provide a visual aid to the driver. Image enhancement specific for infrared images is also implemented and evaluated, based on the Contrast Lim- ited Adaptive Histogram Equalization (CLAHE). An embedded SLAM algorithm is presented with different hardware acceler- ators (point detection, landmark tracking, active search, correlation, matrix operations). All the algorithms were simulated, implemented and verified using as target FPGAs. The validation was done using development kits. A custom board integrating all the presented algorithms is presented. Virtual components developed in this thesis were used in three different projects: PICASSO (stereo vision), COMMROB (obstacle detection from a multi-cameras system) and SART (multispectral vision).

Cette thèse a été réalisée dans le secteur de l'industrie automobile, en collaboration avec le Groupe Renault et concerne en particulier le développement de systèmes d'aide à la conduite avancés et de véhicules autonomes. Les progrès réalisés par la communauté scientifique durant les dernières décennies, dans les domaines de l'informatique et de la robotique notamment, ont été si importants qu'ils permettent aujourd'hui la mise en application de systèmes complexes au sein des véhicules. Ces systèmes visent dans un premier temps à réduire les risques inhérents à la conduite en assistant les conducteurs, puis dans un second temps à offrir des moyens de transport entièrement autonomes. Les méthodes de SLAM multi-objets actuellement intégrées au sein de ces véhicules reposent pour majeure partie sur l'utilisation de capteurs embarqués très performants tels que des télémètres laser, au coût relativement élevé. Les caméras numériques en revanche, de par leur coût largement inférieur, commencent à se démocratiser sur certains véhicules de grande série et assurent généralement des fonctions d'assistance à la conduite, pour l'aide au parking ou le freinage d'urgence, par exemple. En outre, cette implantation plus courante permet également d'envisager leur utilisation afin de reconstruire l'environnement dynamique proche des véhicules en trois dimensions. D'un point de vue scientifique, les techniques de SLAM visuel multi-objets existantes peuvent être regroupées en deux catégories de méthodes. La première catégorie et plus ancienne historiquement concerne les méthodes stéréo, faisant usage de plusieurs caméras à champs recouvrants afin de reconstruire la scène dynamique observée. La plupart reposent en général sur l'utilisation de paires stéréo identiques et placées à faible distance l'une de l'autre, ce qui permet un appariement dense des points d'intérêt dans les images et l'estimation de cartes de disparités utilisées lors de la segmentation du mouvement des points reconstruits. L'autre catégorie de méthodes, dites monoculaires, ne font usage que d'une unique caméra lors du processus de reconstruction. Cela implique la compensation du mouvement propre du système d'acquisition lors de l'estimation du mouvement des autres objets mobiles de la scène de manière indépendante. Plus difficiles, ces méthodes posent plusieurs problèmes, notamment le partitionnement de l'espace de départ en plusieurs sous-espaces représentant les mouvements individuels de chaque objet mobile, mais aussi le problème d'estimation de l'échelle relative de reconstruction de ces objets lors de leur agrégation au sein de la scène statique. La problématique industrielle de cette thèse, consistant en la réutilisation des systèmes multi-caméras déjà implantés au sein des véhicules, majoritairement composés d'un caméra frontale et de caméras surround équipées d'objectifs très grand angle, a donné lieu au développement d'une méthode de reconstruction multi-objets adaptée aux systèmes multi-caméras hétérogènes en stéréo wide-baseline. Cette méthode est incrémentale et permet la reconstruction de points mobiles éparses, grâce notamment à plusieurs contraintes géométriques de segmentation des points reconstruits ainsi que de leur trajectoire. Enfin, une évaluation quantitative et qualitative des performances de la méthode a été menée sur deux jeux de données distincts, dont un a été développé durant ces travaux afin de présenter des caractéristiques similaires aux systèmes hétérogènes existants. / This Ph.D. thesis, which has been carried out in the automotive industry in association with Renault Group, mainly focuses on the development of advanced driver-assistance systems and autonomous vehicles. The progress made by the scientific community during the last decades in the fields of computer science and robotics has been so important that it now enables the implementation of complex embedded systems in vehicles. These systems, primarily designed to provide assistance in simple driving scenarios and emergencies, now aim to offer fully autonomous transport. Multibody SLAM methods currently used in autonomous vehicles often rely on high-performance and expensive onboard sensors such as LIDAR systems. On the other hand, digital video cameras are much cheaper, which has led to their increased use in newer vehicles to provide driving assistance functions, such as parking assistance or emergency braking. Furthermore, this relatively common implementation now allows to consider their use in order to reconstruct the dynamic environment surrounding a vehicle in three dimensions. From a scientific point of view, existing multibody visual SLAM techniques can be divided into two categories of methods. The first and oldest category concerns stereo methods, which use several cameras with overlapping fields of view in order to reconstruct the observed dynamic scene. Most of these methods use identical stereo pairs in short baseline, which allows for the dense matching of feature points to estimate disparity maps that are then used to compute the motions of the scene. The other category concerns monocular methods, which only use one camera during the reconstruction process, meaning that they have to compensate for the ego-motion of the acquisition system in order to estimate the motion of other objects. These methods are more difficult in that they have to address several additional problems, such as motion segmentation, which consists in clustering the initial data into separate subspaces representing the individual movement of each object, but also the problem of the relative scale estimation of these objects before their aggregation within the static scene. The industrial motive for this work lies in the use of existing multi-camera systems already present in actual vehicles to perform dynamic scene reconstruction. These systems, being mostly composed of a front camera accompanied by several surround fisheye cameras in wide-baseline stereo, has led to the development of a multibody reconstruction method dedicated to such heterogeneous systems. The proposed method is incremental and allows for the reconstruction of sparse mobile points as well as their trajectory using several geometric constraints. Finally, a quantitative and qualitative evaluation conducted on two separate datasets, one of which was developed during this thesis in order to present characteristics similar to existing multi-camera systems, is provided.

Les systèmes d'aide à la conduite et les travaux concernant le véhicule autonome ont atteint une certaine maturité durant ces dernières aimées grâce à l'utilisation de technologies avancées. Une étape fondamentale pour ces systèmes porte sur l'estimation du mouvement et de la structure de l'environnement (Structure From Motion) pour accomplir plusieurs tâches, notamment la détection d'obstacles et de marquage routier, la localisation et la cartographie. Pour estimer leurs mouvements, de tels systèmes utilisent des capteurs relativement chers. Pour être commercialisés à grande échelle, il est alors nécessaire de développer des applications avec des dispositifs bas coûts. Dans cette optique, les systèmes de vision se révèlent une bonne alternative. Une nouvelle méthode basée sur des approches 2D/2D à partir d'un réseau de caméras asynchrones est présentée afin d'obtenir le déplacement et la structure 3D à l'échelle absolue en prenant soin d'estimer les facteurs d'échelle. La méthode proposée, appelée méthode des triangles, se base sur l'utilisation de trois images formant un triangle : deux images provenant de la même caméra et une image provenant d'une caméra voisine. L'algorithme admet trois hypothèses: les caméras partagent des champs de vue communs (deux à deux), la trajectoire entre deux images consécutives provenant d'une même caméra est approximée par un segment linéaire et les caméras sont calibrées. La connaissance de la calibration extrinsèque entre deux caméras combinée avec l'hypothèse de mouvement rectiligne du système, permet d'estimer les facteurs d'échelle absolue. La méthode proposée est précise et robuste pour les trajectoires rectilignes et présente des résultats satisfaisants pour les virages. Pour affiner l'estimation initiale, certaines erreurs dues aux imprécisions dans l'estimation des facteurs d'échelle sont améliorées par une méthode d'optimisation : un ajustement de faisceaux local appliqué uniquement sur les facteurs d'échelle absolue et sur les points 3D. L'approche présentée est validée sur des séquences de scènes routières réelles et évaluée par rapport à la vérité terrain obtenue par un GPS différentiel. Une application fondamentale dans les domaines d'aide à la conduite et de la conduite automatisée est la détection de la route et d'obstacles. Pour un système asynchrone, une première approche pour traiter cette application est présentée en se basant sur des cartes de disparité éparses. / Driver assistance systems and autonomous vehicles have reached a certain maturity in recent years through the use of advanced technologies. A fundamental step for these systems is the motion and the structure estimation (Structure From Motion) that accomplish several tasks, including the detection of obstacles and road marking, localisation and mapping. To estimate their movements, such systems use relatively expensive sensors. In order to market such systems on a large scale, it is necessary to develop applications with low cost devices. In this context, vision systems is a good alternative. A new method based on 2D/2D approaches from an asynchronous multi-camera network is presented to obtain the motion and the 3D structure at the absolute scale, focusing on estimating the scale factors. The proposed method, called Triangle Method, is based on the use of three images forming a. triangle shape: two images from the same camera and an image from a neighboring camera. The algorithrn has three assumptions: the cameras share common fields of view (two by two), the path between two consecutive images from a single camera is approximated by a line segment, and the cameras are calibrated. The extrinsic calibration between two cameras combined with the assumption of rectilinear motion of the system allows to estimate the absolute scale factors. The proposed method is accurate and robust for straight trajectories and present satisfactory results for curve trajectories. To refine the initial estimation, some en-ors due to the inaccuracies of the scale estimation are improved by an optimization method: a local bundle adjustment applied only on the absolute scale factors and the 3D points. The presented approach is validated on sequences of real road scenes, and evaluated with respect to the ground truth obtained through a differential GPS. Finally, another fundamental application in the fields of driver assistance and automated driving is road and obstacles detection. A method is presented for an asynchronous system based on sparse disparity maps

The ability to accurately map and localize relevant objects surrounding a vehicle is an important task for autonomous vehicle systems. Currently, many of the environmental mapping approaches rely on the expensive LiDAR sensor. Researchers have been attempting to transition to cheaper sensors like the camera, but so far, the mapping accuracy of single-camera and dual-camera systems has not matched the accuracy of LiDAR systems. This thesis examines depth estimation algorithms and camera configurations of a triple-camera system to determine if sensor data from an additional perspective will improve the accuracy of camera-based systems. Using a synthetic dataset, the performance of a selection of stereo depth estimation algorithms is compared to the performance of two triple-camera depth estimation algorithms: disparity fusion and cost fusion. The cost fusion algorithm in both a multi-baseline and multi-axis triple-camera configuration outperformed the environmental mapping accuracy of non-CNN algorithms in a two-camera configuration.

The use of media technology to pre-program the camera work of performances (with tools such as CuePilot) became a state-of-theart solution in live broadcasted music competitions and events, allowing to create technologically advanced and more appealing live performances. In this paper we study how the use of such solutions affect the viewer’s experience, assuming that preprogrammed camera work results in a more unified experience compared to manual camera work. The paper also investigates whether pre-programmed camera work is noticeable by the viewers. To study these effects an experiment was conducted. The material used was a set of four entries from the Latvian music competition Supernova available in two versions: one produced manually without CuePilot and one pre-programmed with CuePilot. Each participant watched two of the entries without CuePilot and two with, and provided quantitative input according to GEMS (Geneva Emotional Music Scale), an instrument developed in order to measure musically evoked emotions, as well as qualitative input, as each participant had to determine which two of the performances watched were directed with CuePilot and asked to explain their choices. An analysis of the data using statistical tools and significance tests showed that pre-programmed camera work can result in a more unified experience compared to manual camera work, up to some degree. The ability to decrease the variance depends on the creativity value of the Creative Space of the specific production. Pre-programmed camera work is not directly noticeable but can be identified more easily with the presence of video effects, quick cuts or explicit interaction of the artist with the camera. / Användningen av medieteknik för att förprogrammerad kameraarbetet för framträdanden (med verktyg såsom CuePilot) har blivit en lösning i teknologisk framkant inom direktsända musiktävlingar och event vilket möjliggjort tekniskt avancerade och mer attraktiva liveframföranden. I denna uppsats undersöks hur användningen av sådana lösningar påverkar tittarens upplevelse under antagande att förprogrammerat kameraarbete resulterar i en mer enad upplevelse i jämförelse med manuellt kameraarbete. Vidare undersöker uppsatsen om förprogrammerat kameraarbete är märkbart hos tittaren. För att undersöka denna inverkan utfördes ett experiment. Materialet som användes var fyra bidrag från den lettiska musiktävlingen Supernova tillgängliga i två versioner: en producerad manuellt utan CuePilot och en förprogrammerad med CuePilot. Varje deltagare tittade på två av bidragen utan CuePilot och två med och bidrog med kvantitativ input enligt GEMS (Geneva Emotional Music Scale), ett verktyg framtaget för att mäta musikaliskt framkallade känslor, tillsammans med kvalitativ input då deltagarna också blev ombedda att gissa vilka av de sedda bidragen som producerats med CuePilot tillsammans med en motivering av valet. Med statistiska verktyg och signifikanstest visade en analys av datan att förprogrammerat kameraarbete till viss del kan resultera i en mindre varians av upplevelser. Möjligheten att minska variansen beror på kreativitetsvärdet av den kreativa rymden (Creative Space) för en specifik produktion. Förprogrammerat kameraarbete är inte direkt noterbart men kan lättare bli identifierat om videoeffekter, snabba klippningar och tydlig interaktion mellan artist och kamera är närvarande.

In den letzten Jahren sind immer mehr erschwingliche Kamera-Sensoren mit einer zunehmenden Vielfalt optischer Abbildungsfunktionen verfügbar geworden. Low-Cost-Optiken können aufgrund höherer Toleranzen und unterschiedlicher optischer Materialien von der gewünschten Lochkamera Metrik abweichen. Weitwinkel- und Fischaugenobjektive, verzerrende katadioptrische Objektive (spiegelnd und refraktiv) und andere ungewöhnliche Objektive weichen von der Annahme des Modells einer Lochkamera mit einer Brennweite ab. Actionkameras können die gesamte Umgebung mit zwei Objektiven abbilden, diese entsprechen meist nicht mehr dem Lochkameramodell. Kameras werden auch für Messaufgaben hinter zusätzlichen optischen Elementen eingesetzt. Die vorliegende Arbeit erweitert die ersten Erkenntnisse im Bereich der differenzierbaren (glatten) Kameramodelle ohne Einschränkungen. Viele existierende Modelle sind auf bestimmte Objektivtypen spezialisiert. In dieser Arbeit werden mehrere solcher allgemeinen Modelle eingeführt, ohne dass eine global feste Brennweite und spezielle Anforderungen an die Symmetrie der Abbildung erforderlich sind. Eine Einführung alternativer Fehlermetriken im Objektraum bringt auch enorme Rechenvorteile, da eine Abbildungsrichtung analytisch berechnet und viele der Berechnungsergebnisse konstant gehalten werden können. Zur Initialisierung solcher Modelle wird in dieser Arbeit eine generische lineare Kamera vorgestellt. Das wesentliche Merkmal dabei ist eine künstliche Transformation in höhere Dimensionen, welche mit linearen Verfahren weiterverwendet werden. Sie modellieren bereits nichtlineare Verzerrungen und Asymmetrien. Eine Multikamera-Kalibrierungssoftware wird ebenfalls beschrieben und implementiert. Das Ergebnis der Arbeit ist ein theoretischer Rahmen für glatte Kameramodelle im Objektraum selbst – anstelle der Abbildung in den Bildraum – mit mehreren konkreten Modellvorschlägen, Implementierungen und dem angepassten und erweiterten Kalibrierungsprozess. / In recent years, more and more affordable camera sensors with an increasing variety of optical imaging features have become available. Low-cost optics may deviate from the desired pinhole metric due to higher tolerances and different optical materials. Wide-angle and fisheye lenses, distorting catadioptric lenses (specular and refractive) and other unusual lenses deviate from the single focal pinhole camera model assumption, which is sometimes intentional. Action cameras can map the entire environment using two lenses, these usually no longer correspond to the pinhole camera model. Cameras are also used for measuring tasks behind additional optical elements – with unforeseeable deviations in the line of sight. The present work expands the first findings in the field of differentiable (smooth) camera models without constraints. Many existing models specialise in certain types of lenses. In this work, several such general models are introduced without requiring fixed global focal length and symmetry requirements. An introduction of alternative error metrics in the object space also gives enormous computational advantages, since one imaging direction can be calculated analytically and many of the calculation results can be kept constant. For the generation of meaningful starting values of such models, this work introduces a generic linear camera. The essential feature of is an artificial transformation into higher dimensions. These transformed coordinates can then continue to be used with linear methods. They already model non-linear distortions and asymmetries. A multi-camera calibration software that efficiently implements these models is also described and implemented. The result of the work is a theoretical framework for smooth camera models in the object space itself - instead of the established mapping into the image space - with several concrete model proposals, implementations and the adapted and extended calibration process.

<div>LiDAR units onboard airborne and terrestrial platforms have been established as a proven technology for the acquisition of dense point clouds for a wide range of applications, such as digital building model generation, transportation corridor monitoring, precision agriculture, and infrastructure monitoring. Furthermore, integrating such systems with one or more cameras would allow forward and backward projection between imagery and LiDAR data, thus facilitating several high-level data processing activities such as reliable feature extraction and colorization of point clouds. However, the attainment of the full 3D point positioning potential of such systems is contingent on an accurate calibration of the mobile mapping unit as a whole. </div><div> </div><div> This research aims at proposing a calibration procedure for terrestrial multi-unit LiDAR systems to directly estimate the mounting parameters relating several spinning multi-beam laser scanners to the onboard GNSS/INS unit in order to derive point clouds with high positional accuracy. To ensure the accuracy of the estimated mounting parameters, an optimal configuration of target primitives and drive-runs is determined by analyzing the potential impact of bias in mounting parameters of a LiDAR unit on the resultant point cloud for different orientations of target primitives and different drive-run scenarios. This impact is also verified experimentally by simulating a bias in each mounting parameter separately. Next, the optimal configuration is used within an experimental setup to evaluate the performance of the proposed calibration procedure. Then, this proposed multi-unit LiDAR system calibration strategy is extended for multi-LiDAR multi-camera systems in order to allow a simultaneous estimation of the mounting parameters relating the different laser scanners as well as cameras to the onboard GNSS/INS unit. Such a calibration improves the registration accuracy of point clouds derived from LiDAR data and imagery, along with their accuracy with respect to the ground truth. Finally, in order to qualitatively evaluate the calibration results for a generic mobile mapping system and allow the visualization of point clouds, imagery data, and their registration quality, an interface denoted as Image-LiDAR Interactive Visualization Environment (I-LIVE) is developed. Apart from its visualization functions (such as 3D point cloud manipulation and image display/navigation), I-LIVE mainly serves as a tool for the quality control of GNSS/INS-derived trajectory and LiDAR-camera system calibration. </div><div> </div><div> The proposed multi-sensor system calibration procedures are experimentally evaluated by calibrating several mobile mapping platforms with varying number of LiDAR units and cameras. For all cases, the system calibration is seen to attain accuracies better than the ones expected based on the specifications of the involved hardware components, i.e., the LiDAR units, cameras, and GNSS/INS units.</div>

This thesis presents an approach for configuring and calibrating a network of RGB-D sensors used to guide a robotic arm to interact with objects that get rapidly modeled in 3D. The system is based on Microsoft Kinect sensors for 3D data acquisition. The work presented here also details an analysis and experimental study of the Kinect’s depth sensor capabilities and performance. The study comprises examination of the resolution, quantization error, and random distribution of depth data. In addition, the effects of color and reflectance characteristics of an object are also analyzed. The study examines two versions of Kinect sensors, one dedicated to operate with the Xbox 360 video game console and the more recent Microsoft Kinect for Windows version. The study of the Kinect sensor is extended to the design of a rapid acquisition system dedicated to large workspaces by the linkage of multiple Kinect units to collect 3D data over a large object, such as an automotive vehicle. A customized calibration method for this large workspace is proposed which takes advantage of the rapid 3D measurement technology embedded in the Kinect sensor and provides registration accuracy between local sections of point clouds that is within the range of the depth measurements accuracy permitted by the Kinect technology. The method is developed to calibrate all Kinect units with respect to a reference Kinect. The internal calibration of the sensor in between the color and depth measurements is also performed to optimize the alignment between the modalities. The calibration of the 3D vision system is also extended to formally estimate its configuration with respect to the base of a manipulator robot, therefore allowing for seamless integration between the proposed vision platform and the kinematic control of the robot. The resulting vision-robotic system defines the comprehensive calibration of reference Kinect with the robot. The latter can then be used to interact under visual guidance with large objects, such as vehicles, that are positioned within a significantly enlarged field of view created by the network of RGB-D sensors. The proposed design and calibration method is validated in a real world scenario where five Kinect sensors operate collaboratively to rapidly and accurately reconstruct a 180 degrees coverage of the surface shape of various types of vehicles from a set of individual acquisitions performed in a semi-controlled environment, that is an underground parking garage. The vehicle geometrical properties generated from the acquired 3D data are compared with the original dimensions of the vehicle.

This thesis presents an approach for configuring and calibrating a network of RGB-D sensors used to guide a robotic arm to interact with objects that get rapidly modeled in 3D. The system is based on Microsoft Kinect sensors for 3D data acquisition. The work presented here also details an analysis and experimental study of the Kinect’s depth sensor capabilities and performance. The study comprises examination of the resolution, quantization error, and random distribution of depth data. In addition, the effects of color and reflectance characteristics of an object are also analyzed. The study examines two versions of Kinect sensors, one dedicated to operate with the Xbox 360 video game console and the more recent Microsoft Kinect for Windows version. The study of the Kinect sensor is extended to the design of a rapid acquisition system dedicated to large workspaces by the linkage of multiple Kinect units to collect 3D data over a large object, such as an automotive vehicle. A customized calibration method for this large workspace is proposed which takes advantage of the rapid 3D measurement technology embedded in the Kinect sensor and provides registration accuracy between local sections of point clouds that is within the range of the depth measurements accuracy permitted by the Kinect technology. The method is developed to calibrate all Kinect units with respect to a reference Kinect. The internal calibration of the sensor in between the color and depth measurements is also performed to optimize the alignment between the modalities. The calibration of the 3D vision system is also extended to formally estimate its configuration with respect to the base of a manipulator robot, therefore allowing for seamless integration between the proposed vision platform and the kinematic control of the robot. The resulting vision-robotic system defines the comprehensive calibration of reference Kinect with the robot. The latter can then be used to interact under visual guidance with large objects, such as vehicles, that are positioned within a significantly enlarged field of view created by the network of RGB-D sensors. The proposed design and calibration method is validated in a real world scenario where five Kinect sensors operate collaboratively to rapidly and accurately reconstruct a 180 degrees coverage of the surface shape of various types of vehicles from a set of individual acquisitions performed in a semi-controlled environment, that is an underground parking garage. The vehicle geometrical properties generated from the acquired 3D data are compared with the original dimensions of the vehicle.