Estimating the Fundamental Matrix via Constrained Least-Squares: A Convex Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Noise Induced Errors in Camera Positioning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera Displacement via Constrained Minimization of the Algebraic Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual servoing for large camera displacements
IEEE Transactions on Robotics
Keeping features in the field of view in eye-in-hand visual servoing: a switching approach
IEEE Transactions on Robotics
Point-based and region-based image moments for visual servoing of planar objects
IEEE Transactions on Robotics
IEEE Transactions on Robotics
Ensuring visibility in calibration-free path planning for image-based visual servoing
IEEE Transactions on Robotics
Global Path-Planning for Constrained and Optimal Visual Servoing
IEEE Transactions on Robotics
Optimal object configurations to minimize the positioning error in visual servoing
IEEE Transactions on Robotics
Formal modeling of robot behavior with learning
Neural Computation
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Visual servoing consists of positioning a robot end-effector based on the matching of some object features in the image. However, due to the presence of image noise, this matching can never be ensured, hence introducing an error on the final location of the robot. This paper addresses the problem of estimating the worst-case location error introduced by image points matching. In particular, we propose some strategies for computing upper bounds and lower bounds of such an error according to several possible measures for certain image noise intensity and camera-object configuration. These bounds provide an admissible region of the sought worst-case location error, and hence allow one to establish performance limitation of visual servo systems. Some examples are reported to illustrate the proposed strategies and their results.