Relative end-effector control using Cartesian position based visual servoing

Wilson, William J.; Hulls, Carol; Bell, Gregory

doi:10.1109/70.538974

Cited by 552 publications

(292 citation statements)

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“…Control inputs are either computed directly by a comparison of the current and reference image Segvic et al, 2009) or by a computation of camera coordinates in the world reference frame (Wilson et al, 1996;DeMenthon and Davis, 1992). Normally, the visual servoing relies on a geometrical approach to calculate relations of map landmarks to the current image salient points (Segvic et al, 2009).…”

Section: Vision-based Navigationmentioning

confidence: 99%

Simple yet stable bearing‐only navigation

Krajník

Faigl

Vonásek

et al. 2010

Journal of Field Robotics

108

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This article describes a simple monocular navigation system for a mobile robot based on the map-and-replay technique. The presented method is robust, easy to implement, does not require sensor calibration or structured environment and its computational complexity is independent of the environment size. The method can navigate a robot while sensing only one landmark at a time, making it more robust than other monocular approaches. The aforementioned properties of the method allow even low-cost robots to effectively act in large outdoor and indoor environments with natural landmarks only.The basic idea is to utilize a monocular vision to correct only the robot's heading and leaving distance measurements just to the odometry. The heading correction itself can suppress the odometric error and prevent the overall position error from diverging.The influence of a map-based heading estimation and odometric errors on the overall position uncertainty is examined. A claim that for closed polygonal trajectories the position error of this type of navigation does not diverge is stated. The claim is defended mathematically and experimentally. The method has been experimentally tested in a set of indoor and outdoor experiments, during which the average position errors have been lower than 0.3 m for paths over 1 km long.

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Section: Vision-based Navigationmentioning

confidence: 99%

Simple yet stable bearing‐only navigation

Krajník

Faigl

Vonásek

et al. 2010

Journal of Field Robotics

108

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“…Position-based visual servoing belongs to this approach. Using visual measures and an a priori knowledge of a CAD model of the observed object, the relative cameraobject pose is estimated and used as input to the control scheme [33]. An adequate 3D trajectory can thus be obtained, such as a geodesic for the orientation and a straight line for the translation.…”

Section: Introduction T O Date An Open Question In Image-based VImentioning

confidence: 99%

Point-based and region-based image moments for visual servoing of planar objects

2005

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To cite this version:O. Tahri, François Chaumette. Point-based and region-based image moments for visual servoing of planar objects. IEEE Transactions on Robotics, IEEE, 2005, 21 (6) Abstract-Moments are generic (and usually intuitive) descriptors that can be computed from several kinds of objects defined either from closed contours or from a set of points. In this paper, we present improvements in image-based visual servo using image moments. First, the analytical form of the interaction matrix related to the moments computed from a set of coplanar points is derived, and we show that it is different of the form obtained previously using coplanar closed contours. Six visual features are selected to design a decoupled control scheme when the object is parallel to the image plane. This nice property is then generalized to the case where the desired object position is not parallel to the image plane. Finally, experimental results are presented to illustrate the validity of our approach and its robustness with respect to modeling errors.

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“…to the specification and the realization of the task at hand. 3D visual servoing [30] could also be considered but, in that case, instability and errors in pose computation may become a major problem.…”

Section: Tracking Within Visual Servoing Experimentsmentioning

confidence: 99%