Visual Servoing from Deep Neural Networks

We present a deep neural network-based method to perform high-precision, robust and real-time 6 DOF visual servoing. The paper describes how to create a dataset simulating various perturbations (occlusions and lighting conditions) from a single real-world image of the scene. A convolutional neural network is fine-tuned using this dataset to estimate the relative pose between two images of the same scene. The output of the network is then employed in a visual servoing control scheme. The method converges robustly even in difficult real-world settings with strong lighting variations and occlusions.A positioning error of less than one millimeter is obtained in experiments with a 6 DOF robot.Comment: fixed authors lis

Visual Servoing using the Sum of Conditional Variance

International audienceIn this paper we propose a new way to achieve direct visual servoing. The novelty is the use of the sum of conditional variance to realize the optimization process of a positioning task. This measure, which has previously been used successfully in the case of visual tracking, has been shown to be invariant to non-linear illumination variations and inexpensive to compute. Compared to other direct approaches of visual servoing, it is a good compromise between techniques using the illumination of pixels which are computationally inexpensive but non robust to illumination variations and other approaches using the mutual information which are more complicated to compute but offer more robustness towards the variations of the scene. This method results in a direct visual servoing task easy and fast to compute and robust towards non-linear illumination variations. This paper describes a visual servoing task based on the sum of conditional variance performed using a Levenberg-Marquardt optimization process. The results are then demonstrated through experimental validations and compared to both photometric-based and entropy-based techniques

A combination of particle filtering and deterministic approaches for multiple kernel tracking.

International audienceColor-based tracking methods have proved to be efficient for their robustness qualities. The drawback of such global representation of an object is the lack of information on its spatial configuration, making difficult the tracking of more complex motions. This issue is overcome by using several kernels weighting pixels locations. In this paper a multiple kernels configuration is proposed and developed in both probabilistic and deterministic frameworks. The advantages of both approaches are combined to design a robust tracker allowing to track location, size and orientation of the object. A visual servoing application in tracking a moving object validates the proposed approach

Dense non-rigid visual tracking with a robust similarity function

International audienceThis paper deals with dense non-rigid visual tracking robust towards global illumination perturbations of the observed scene. The similarity function is based on the sum of condi- tional variance (SCV). With respect to most approaches that minimize the sum of squared differences, which is poorly robust towards illumination variations in the scene, the choice of SCV as our registration function allows the approach to be naturally robust towards global perturbations. Moreover, a thin-plate spline warping function is considered in order to take into account deformations of the observed template. The proposed approach, after being detailed, is tested in nominal conditions and on scenes where light perturbations occur in order to assess the robustness of the approach

Coupled oxidation–reduction of butanol–hexanal by resting Rhodococcus erythropolis NCIMB 13064 cells in liquid and gas phases

Rhodococcus erythropolis is a promising Gram-positive bacterium capable of numerous bioconversions including those involving alcohol dehydrogenases (ADHs). In this work, we compared and optimized the redox biocatalytic performances of 1-butanol-grown R. erythropolis NCIMB 13064 cells in aqueous and in non-conventional gas phase using the 1-butanol–hexanal oxidation–reduction as model reaction. Oxidation of 1-butanol to butanal is tightly coupled to the reduction of hexanal to 1-hexanol at the level of a nicotinoprotein–ADH-like enzyme. Cell viability is dispensable for reaction. In aqueous batch conditions, fresh and lyophilized cells are efficient redox catalysts (oxidation–reduction rate = 76 micromol min−1 g cell dry mass−1) being also reactive towards benzyl alcohol, (S)-2-pentanol, and geraniol as reductants. However, butanol hexanal oxidation–reduction is strongly limited by product accumulation and by hexanal toxicity that is amajor factor influencing cell behavior and performance. Reaction rate is maximal at 40 ◦C pH 7.0 in aqueous phase and at 60 ◦C- pH 7.0–9.0 in gas phase. Importantly, lyophilized cells also showed to be promising redox catalysts in the gas phase (at least 65 micromol min−1 g cell dry mass−1). The system is notably stable for several days at moderate thermodynamic activities of hexanal (0.06–0.12), 1-butanol (0.12) and water (0.7)

Hybrid tracking approach using optical flow and pose estimation

International audienceThis paper proposes an hybrid approach to estimate the 3D pose of an object. The integration of texture information based on image intensities in a more classical non-linear edge-based pose estimation computation has proven to highly increase the reliability of the tracker. We propose in this work to exploit the data provided by an optical flow algorithm for a similar purpose. The advantage of using the optical flow is that it does not require any a priori knowledge on the object appearance. The registration of 2D and 3D cues for monocular tracking is performed by a non linear minimization. Results obtained show that using optical flow enables to perform robust 3D hybrid tracking even without any texture mode

Highly precise micropositioning task using a direct visual servoing scheme.

International audienceThis paper demonstrates a precise micropositioning scheme based on a direct visual servoing process. This technique uses only the pure image signal (photometric information) to design the control law. With respect to traditional visual servoing approaches that use geometric visual features (points, lines, ...), the visual features used in the control law is nothing but the pixel luminance. The proposed approach was tested in term of precision and robustness in several experimental conditions. The obtained results have demonstrated a good behavior of the control law and very good positioning precision. The obtained precisions are 89 nanometers, 14 nanometers, and 0.001 degrees in the x, y and axes of positioning platform, respectively