Path Planning for Robust Image-Based Visual Servoing

Vision feedback control loop techniques are efficient for a large class of applications but they come up against difficulties when the initial and desired robot positions are distant. Classical approaches are based on the regulation to zero of an error function computed from the current measurement and a constant desired one. By using such approach, it is not obvious to introduce any constraint in the realized trajectories and to ensure the convergence for all the initial configurations. In this paper, we propose a new approach to resolve these difficulties by coupling path planning in image space and image-based control. Constraints such that the object remains in the camera field of view or the robot avoids its joint limits can be taken into account at the task planning level. Furthermore, by using this approach, current measurements always remain close to their desired value and a control by image-based servoing ensures the robustness with respect to modeling errors. The proposed method is based on the potential field approach and is applied when object shape and dimensions are known or not, and when the calibration parameters of the camera are well or badly estimated. Finally, real time experimental results using an eye-in-hand robotic system are presented and confirm the validity of our approach

Visual servoed micropositioning for protein manipulation tasks

In this paper, we present a framework for cell manipulation tasks with visual servoing micromanipulation strategies. A vision based micropositioner is designed in order to address the requirement of high precision needed to perform manipulation of objects under 100 μm in size. The system calibration (microscope-camera-micropositioner) and the model of the observed scene are not known. Experimental results for micropositioning tasks with respect to protein cells are presented and demonstrate the validity of the proposed approach

Generic decoupled image-based visual servoing for cameras obeying the unified projection model

In this paper a generic decoupled imaged-based control scheme for calibrated cameras obeying the unified projection model is proposed. The proposed decoupled scheme is based on the surface of object projections onto the unit sphere. Such features are invariant to rotational motions. This allows the control of translational motion independently from the rotational motion. Finally, the proposed results are validated with experiments using a classical perspective camera as well as a fisheye camera mounted on a 6 dofs robot platform

Rotation Free Active Vision

International audience— Incremental Structure from Motion (SfM) algorithms require, in general, precise knowledge of the camera linear and angular velocities in the camera frame for estimating the 3D structure of the scene. Since an accurate measurement of the camera own motion may be a non-trivial task in several robotics applications (for instance when the camera is onboard a UAV), we propose in this paper an active SfM scheme fully independent from the camera angular velocity. This is achieved by considering, as visual features, some rotational invariants obtained from the projection of the perceived 3D points onto a virtual unitary sphere (unified camera model). This feature set is then exploited for designing a rotation-free active SfM algorithm able to optimize online the direction of the camera linear velocity for improving the convergence of the structure estimation task. As case study, we apply our framework to the depth estimation of a set of 3D points and discuss several simulations and experimental results for illustrating the approach

Omnidirectional Vision-Based Control from Homography

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Model-free optimal trajectories in the image space

International audienceSince image-based servoing is a local control solution, it requires the definition of intermediate subgoals in the sensor space. This paper addresses the problem of generating realistic and optimal smooth trajectories of complex features in the image space. The model of the observed target and the internal camera parameters are assumed to be unknown. First, a closed-form smooth collineation path between given starts and endpoints is obtained. This path is generated in order to correspond to an optimal camera path. The trajectories of the image features are then derive

Loop closure for topological mapping and navigation with omnidirectional images

Dans le cadre de la robotique mobile, des progrès significatifs ont été obtenus au cours des trois dernières décennies pour la cartographie et la localisation. La plupart des projets de recherche traitent du problème de SLAM métrique. Les techniques alors développées sont sensibles aux erreurs liées à la dérive ce qui restreint leur utilisation à des environnements de petite échelle. Dans des environnements de grande taille, l utilisation de cartes topologiques, qui sont indépendantes de l information métrique, se présentent comme une alternative aux approches métriques.Cette thèse porte principalement sur le problème de la construction de cartes topologiques pour la navigation de robots mobiles dans des environnements urbains de grande taille, en utilisant des caméras omnidirectionnelles. La principale contribution de cette thèse est la résolution efficace et avec précision du problème de fermeture de boucles, problème qui est au coeur de tout algorithme de cartographie topologique. Le cadre de cartographie topologique éparse / hiérarchique proposé allie une approche de partionnement de séquence d images (ISP) par regroupement des images visuellement similaires dans un noeud avec une approche de détection de fermeture de boucles permettant de connecter ces noeux. Le graphe topologique alors obtenu représente l environnement du robot. L algorithme de fermeture de boucle hiérarchique développé permet d extraire dans un premier temps les noeuds semblables puis, dans un second temps, l image la plus similaire. Cette détection de fermeture de boucles hiérarchique est rendue efficace par le stockage du contenu des cartes éparses sous la forme d une structure de données d indexation appelée fichier inversé hiérarchique (HIF). Nous proposons de combiner le score de pondération TFIDF avec des contraintes spatiales et la fréquence des amers détectés pour obtenir une meilleur robustesse de la fermeture de boucles. Les résultats en terme de densité et précision des cartes obtenues et d efficacité sont évaluées et comparées aux résultats obtenus avec des approches de l état de l art sur des séquences d images omnidirectionnelles acquises en milieu extérieur. Au niveau de la précision des détections de boucles, des résultats similaires ont été observés vis-à-vis des autres approches mais sans étape de vérification utilisant la géométrie épipolaire. Bien qu efficace, l approche basée sur HIF présente des inconvénients comme la faible densité des cartes et le faible taux de détection des boucles. Une seconde technique de fermeture de boucle a alors été développée pour combler ces lacunes. Le problème de la faible densité des cartes est causé par un sur-partionnement de la séquence d images. Celui-ci est résolu en utilisant des vecteurs de descripteurs agrégés localement (VLAD) lors de l étape de ISP. Une mesure de similarité basée sur une contrainte spatiale spécifique à la structure des images omnidirectionnelles a également été développée. Des résultats plus précis sont obtenus, même en présence de peu d appariements. Les taux de réussite sont meilleurs qu avec FABMAP 2.0, la méthode la plus utilisée actuellement, sans étape supplémentaire de vérification géométrique.L environnement est souvent supposé invariant au cours du temps : la carte de l environnement est construite lors d une phase d apprentissage puis n est pas modifiée ensuite. Une gestion de la mémoire à long terme est nécessaire pour prendre en compte les modifications dans l environnement au cours du temps. La deuxième contribution de cette thèse est la formulation d une approche de gestion de la mémoire visuelle à long terme qui peut être utilisée dans le cadre de cartes visuelles topologiques et métriques. Les premiers résultats obtenus sont encourageants. (...)Over the last three decades, research in mobile robotic mapping and localization has seen significant progress. However, most of the research projects these problems into the SLAM framework while trying to map and localize metrically. As metrical mapping techniques are vulnerable to errors caused by drift, their ability to produce consistent maps is limited to small scale environments. Consequently, topological mapping approaches which are independent of metrical information stand as an alternative to metrical approaches in large scale environments. This thesis mainly deals with the loop closure problem which is the crux of any topological mapping algorithm. Our main aim is to solve the loop closure problem efficiently and accurately using an omnidirectional imaging sensor. Sparse topological maps can be built by representing groups of visually similar images of a sequence as nodes of a topological graph. We propose a sparse / hierarchical topological mapping framework which uses Image Sequence Partitioning (ISP) to group visually similar images of a sequence as nodes which are then connected on occurrence of loop closures to form a topological graph. A hierarchical loop closure algorithm that can first retrieve the similar nodes and then perform an image similarity analysis on the retrieved nodes is used. An indexing data structure called Hierarchical Inverted File (HIF) is proposed to store the sparse maps to facilitate an efficient hierarchical loop closure. TFIDF weighting is combined with spatial and frequency constraints on the detected features for improved loop closure robustness. Sparsity, efficiency and accuracy of the resulting maps are evaluated and compared to that of the other two existing techniques on publicly available outdoor omni-directional image sequences. Modest loop closure recall rates have been observed without using the epi-polar geometry verification step common in other approaches. Although efficient, the HIF based approach has certain disadvantages like low sparsity of maps and low recall rate of loop closure. To address these shortcomings, another loop closure technique using spatial constraint based similarity measure on omnidirectional images has been proposed. The low sparsity of maps caused by over-partitioning of the input sequence has been overcome by using Vector of Locally Aggregated Descriptors (VLAD) for ISP. Poor resolution of the omnidirectional images causes fewer feature matches in image pairs resulting in reduced recall rates. A spatial constraint exploiting the omnidirectional image structure is used for feature matching which gives accurate results even with fewer feature matches. Recall rates better than the contemporary FABMAP 2.0 approach have been observed without the additional geometric verification. The second contribution of this thesis is the formulation of a visual memory management approach suitable for long term operability of mobile robots. The formulated approach is suitable for both topological and metrical visual maps. Initial results which demonstrate the capabilities of this approach have been provided. Finally, a detailed description of the acquisition and construction of our multi-sensor dataset is provided. The aim of this dataset is to serve the researchers working in the mobile robotics and vision communities for evaluating applications like visual SLAM, mapping and visual odometry. This is the first dataset with omnidirectional images acquired on a car-like vehicle driven along a trajectory with multiple loops. The dataset consists of 6 sequences with data from 11 sensors including 7 cameras, stretching 18 kilometers in a semi-urban environmental setting with complete and precise ground-truth.CLERMONT FD-Bib.électronique (631139902) / SudocSudocFranceF

Model-free optimal trajectories in the image space: Application to robot vision control

International audienceSince image-based servoing is a local control solution, it requires the definition of intermediate subgoals in the sensor space when the initial robot position is far from the desired one. This paper addresses the problem of generating and tracking realistic and optimal smooth trajectories of complex features in the image space. The model of the observed target and the internal camera parameters are assumed to be unknown. First a closed-form smooth collineation path (related to a reference plane) between given starts and end-points is obtained. This path is generated in order to correspond to an optimal camera path. The trajectories of the image features (corresponding to points belonging to or not belonging to the reference plane) are then derived and tracked using image based control

Commande Vision/Force de robots parallèles.

National audienceIn this paper, force and position control of parallel kinematic machines is discussed. Cartesian space computed torque control is applied to achieve force and position servoing directly in the task space within a sensor based control architecture. The originality of the approach resides in the use of a vision system as an exteroceptive pose measurement of a parallel machine tool for force control purposes

Efficient high-speed vision-based computed torque control of the orthoglide parallel robot.

International audienceVision has often been considered as not suitable for dynamic control of robots. The experimental results presented in this paper show that it is possible to perform better with a vision based dynamic control than with a modelbased control. These results were obtained using a Cartesian computed torque control fed back, without any joint sensing, by a novel Cartesian pose and velocity estimator. The latter is designed as a virtual visual servoing scheme based on sequential acquisition of sub-images and a constant acceleration motion assumption