Underwater images are altered by the physical characteristics of the medium through which light rays pass before reaching the optical sensor. Scattering and wavelength-dependent absorption significantly modify the captured colors depending on the distance of observed elements to the image plane. In this paper, we aim to recover an image of the scene as if the water had no effect on light propagation. We introduce SUCRe, a novel method that exploits the scene’s 3D structure for underwater color restoration. By following points in multiple images and tracking their intensities at different distances to the sensor, we constrain the optimization of the parameters in an underwater image formation model and retrieve unattenuated pixel intensities. We conduct extensive quantitative and qualitative analyses of our approach in a variety of scenarios ranging from natural light to deep-sea environments using three underwater datasets acquired from real-world scenarios and one synthetic dataset. We also compare the performance of the proposed approach with that of a wide range of existing state-of-the-art methods. The results demonstrate a consistent benefit of exploiting multiple views across a spectrum of objective metrics. Our code is publicly available at github.com/clementinboittiaux/sucre.

This paper examines the relevance of using catenary-based curves to model cables in underwater tethered robotic applications in order to take into account the influence of hydrodynamic damping. To this end, an augmented catenary-based model is introduced to deal with the dynamical effects of surge motion, sway motion or a combination of both on a cable. Experimental studies are carried out with eight cables of varying stiffness, weight and buoyancy. One end of the cable is fixed, while the other end is moved by the underwater robot. The obtained results help to determine which cables and which dynamics are compatible with a fair estimation of the cable shape through the proposed models.

This study is twofold. First, a comprehensive simulation framework of cable dynamics is introduced. This framework considers variable length cables and allows to incorporate elements such as buoys, ballast or Inertial Measurement Unit (IMU) sensors. The accuracy of this framework is assessed through experimental data. Second, a novel and improved solution for the instrumentation of a V-shaped buoy-ballast system using IMU sensors is investigated. This latter, designed for a neutrally buoyant tether between a Remotely Operated Vehicle (ROV) and an Unmanned Surface Vehicle (USV), is meant to improve operation safety. The discussed IMU-based solution provides wider information on the interaction between the ROV and the cable, including its 3D orientation and curvature amplitude, which could be used for both the control of the USV trajectory and its onboard winch.

This paper deals with the estimation of the shape of a catenary for a negatively buoyant cable, connecting a pair of underwater robots in a robot chain. The new estimation method proposed here is based on the calculation of local tangents thanks to the data acquired from inertial measurement units (IMUs), which are attached to the cable near its ends. This method is compared with a vision-based estimation method that was developed previously. Experiments are conducted, in the air and in a pool, using a motion capture system for ground truth. The results obtained show that the new method significantly improves the estimation of the catenary height. Furthermore, the identification of the cable shape is not affected by the limits of the camera's field of view and by the image projection, resulting in increased accuracy and range, without singularities.

Tethers are used to supply power and transfer data for teleoperated robots. They are known to limit the robot's workspace and could have the effect of hampering its motion. What if we could take advantage of the tether? In this paper a new visual servoing scheme for catenary shaped deformable objects is introduced in order to control the tether parametric shape by properly moving its fixation point. In most of the visual servoing approaches the target object is rigid and distant from the controlled robot. On the contrary, in this paper, the object is deformable and attached to the robot, thus its 3D shape changes while the robot is moving. The experimental system is composed of two terrestrial mobile robots of the same motion capabilities linked with a slack rope. Simulation and real experiments validate the proposed control scheme for proper tether handling.

Predicting the behavior of visual features on the image plane over a future time horizon is an important possibility in many different control problems. For example when dealing with occlusions (or other constraints such as joint limits) in a classical visual servoing loop, or also in the more advanced model predictive control schemes recently proposed in the literature. Several possibilities have been proposed to perform the initial correction step for then propagating the visual features by exploiting the measurements currently available by the camera. But the predictions proposed so far are inaccurate in situations where the depths of the tracked points are not correctly estimated. We then propose in this paper a new correction strategy which tries to directly correct the relative pose between the camera and the target instead of only adjusting the error on the image plane. This correction is then analysed and compared by evaluating the corresponding improvements in the feature prediction phase.

This paper introduces a visual servoing scheme for humanoid walking. Though most of the existing approaches follow a perception-decision-action scheme, we close the loop so that the control is robust to model error. Our approach is based on a new reactive pattern generator which modifies, at the control level, the footsteps, the center of mass and the center of pressure trajectories for the center of mass to track a reference velocity. And, in this paper, the reference velocity is directly given by a visual servoing control law. Since, the HRP-2 walk induces a sway motion that disturbs the regulation of the visual control law, we introduce a control law allowing convergence in the image space and taking into account this sway motion.

This paper presents a method to determine the rough shape of an object. This is a step in the development of a ldquoone click grasping toolrdquo, a grasping tool of everyday-life objects for an assistant robot dedicated to elderly or disabled. The goal is to determine the quadric that approximates at best the shape of an unknown object using multi-view measurements. Non-linear optimization techniques are considered to achieve this goal. Since multiple views are necessary, an active vision process is considered in order to minimize the uncertainty on the estimated parameters and determine the next best view. Finally, results that show the validity of the approach are presented.

A critical assumption of many multi-view control systems is the initial visibility of the regions of interest from all the views. An initialization step is proposed for a hybrid eye-in-hand/eye-to-hand grasping system to fulfil this requirement. In this paper, the object of interest is assumed to be within the eye-to-hand field of view, whereas it may not be within the eye-in-hand one. The object model is unknown and no database is used. The object lies in a complex scene with a cluttered background. A method to automatically focus on the object of interest is presented, tested and validated on a multi view robotic system.