Motion capture setups are used in numerous fields. Studies based on motion capture data can be found in biomechanical, sport or animal science. Clinical science studies include gait analysis as well as balance, posture and motor control. Robotic applications encompass object tracking. Today’s life applications includes entertainment or augmented reality. Still, few studies investigate the positioning performance of motion capture setups. In this paper, we study the positioning performance of one player in the optoelectronic motion capture based on markers: Vicon system. Our protocol includes evaluations of static and dynamic performances. Mean error as well as positioning variabilities are studied with calibrated ground truth setups that are not based on other motion capture modalities. We introduce a new setup that enables directly estimating the absolute positioning accuracy for dynamic experiments contrary to state-of-the art works that rely on inter-marker distances. The system performs well on static experiments with a mean absolute error of 0.15 mm and a variability lower than 0.025 mm. Our dynamic experiments were carried out at speeds found in real applications. Our work suggests that the system error is less than 2 mm. We also found that marker size and Vicon sampling rate must be carefully chosen with respect to the speed encountered in the application in order to reach optimal positioning performance that can go to 0.3 mm for our dynamic study.

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In this paper, we explore the different minimal solutions for egomotion estimation of a camera based on homography knowing the gravity vector between calibrated images. These solutions depend on the prior knowledge about the reference plane used by the homography. We then demonstrate that the number of matched points can vary from two to three and that a direct closed-form solution or a Gröbner basis based solution can be derived according to this plane. Many experimental results on synthetic and real sequences in indoor and outdoor environments show the efficiency and the robustness of our approach compared to standard methods.

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Interpretation of the results of this open-label study is limited by the lack of a standard wound dressing as a comparator and by the varying types of wounds selected for inclusion. Nevertheless, the results of this study indicate that hyaluronic acid provides good healing of acute wounds and is well accepted by both patients and nurses.

Previous works have shown that catadioptric systems are particularly suited for egomotion estimation thanks to their large field of view and thus numerous algorithms have already been proposed in the literature to estimate the motion. In this paper, we present a method for estimating six degrees of freedom camera motions from central catadioptric images in man-made environments. State-of-the-art methods can obtain very impressive results. However our proposed system provides two strong advantages over the existing methods: first, it can implicitly handle the di±culty of planar/non-planar scenes, and second, it is computationally much less expensive. The only assumption deals with the presence of parallel straight lines which is reasonable in a man-made environment. More precisely, we estimate the motion by decoupling the rotation and the translation. The rotation is computed by an e±cient algorithm based on the detection of dominant bundles of parallel catadioptric lines and the translation is calculated from a robust 2-point algorithm. We also show that the line-based approach allows to estimate the absolute attitude (roll and pitch angles) at each frame, without error accumulation. The e±ciency of our approach has been validated by experiments in both indoor and outdoor environments and also by comparison with other existing methods.

International audienceRotation estimation is a fundamental step for various robotic applications such as automatic control of ground/aerial vehicles, motion estimation and 3D reconstruction. However it is now well established that traditional navigation equipments, such as global positioning systems (GPSs) or inertial measurement units (IMUs), suffer from several disadvantages. Hence, some vision-based works have been proposed recently. Whereas interesting results can be obtained, the existing methods have non-negligible limitations such as a difficult feature matching (e.g. repeated textures, blur or illumination changes) and a high computational cost (e.g. analyze in the frequency domain). Moreover, most of them utilize conventional perspective cameras and thus have a limited field of view. In order to overcome these limitations, in this paper we present a novel rotation estimation approach based on the extraction of vanishing points in omnidirectional images. The first advantage is that our rotation estimation is decoupled from the translation computation, which accelerates the execution time and results in a better control solution. This is made possible by our complete framework dedicated to omnidirectional vision, whereas conventional vision has a rotation/translation ambiguity. Second, we propose a top-down approach which maintains the important constraint of vanishing point orthogonality by inverting the problem: instead of performing a difficult line clustering preliminary step, we directly search for the orthogonal vanishing points. Finally, experimental results on various data sets for diverse robotic applications have demonstrated that our novel framework is accurate, robust, maintains the orthogonality of the vanishing points and can run in real-time

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