This paper presents how to detect planes from a set of corresponding points with a stereo camera system. We show that, in the presence of two cameras aligned to the same orientation, it is possible to compute the normal vector to a plane by using only three corresponding points whereas with traditional methods this computation requires at least four points and the decomposition of the homography matrix. We obtain the triangulation of the set of features and we compute for every triangle its normal vector. By grouping adjacent triangles with equal normal we are able to select all the points belonging to the same plane. The method does not require any information about the camera calibration or the camera relative translation. Simulation and experiments on real images validate the proposed algorithm

This paper presents an epipolar based visual servoing for mobile robots equipped with a panoramic camera. The proposed visual servoing is based on the epipolar geometry and exploits the auto-epipolar property, a special configuration for the epipoles which occurs when the desired and the current views undergo a pure translation. This occurrence is detectable observing when the bi-osculating mirror conics co-intersect at the two epipoles. The auto epipolar condition enables our controller to retrieve the equal orientation between target and current camera. Translation is performed by exploiting the epipoles. Simulated experiments and Lyapunov-based stability analysis demonstrate the parametric robustness of the proposed method.

We explore the benefits of multiple views for visual servoing (VS) by commanding a single camera to first "peer" at a scene from two vantage points, thus acquiring a set "reference" images, prior to executing a visual position task. Our approach completely decouples the translational and rotational components of our controller: epipoles from the reference views drive the translational error to zero, while the rotational degrees of freedom maintain all of the features in the field of view (FOV). We furnish a simple Lyapunov stability proof that demonstrates a large domain of attraction while maintaining all features in the FOV. Finally, we present simulated experiments that suggest robustness to measurement noise and large variations in the baseline between the reference views.

We present a purely rotational visual servoing algorithm, which aligns the orientation between two cameras at different locations in space. Specifically, our kinematic controller steers a set of so-called bi-tangent lines to intersect at the epipole using a purely image-based bi-tangent line Jacobian. Bi-tangent lines, i.e. lines joining corresponding features on the superposition of two views of a scene, can be defined for both points and contours, so we apply our controller to both feature types. Simulated experiments demonstrate the parametric robustness of the proposed method.

A novel visual servoing method is presented. The algorithm works for both apparent contours and point features and does not require any information about the internal camera parameters. The proposed visual servoing is based on the epipolar geometry and exploits the autoepipolar property, a special configuration for the epipoles which occurs when the desired and current views undergo a pure translation. This occurrence is simply detectable from special line conditions on the overlapped current and desired images. Experiments are presented to validate the proposed visual servoing.

This paper proposes a visual servoing algorithm for hand-eye robotic system based on epipolar geometry. The control law is based on the estimation of the epipoles position obtained by points correspondences extracted from the current and target images. The camera-robot motion is computed from the observation of the epipoles coordinates. Only the principal camera point is assumed to be known but not the other intrinsic parameters. Experimental results are reported to validate the visual servoing algorithm proposed.