Using scale-invariant feature points in visual servoing

Azad Shademan; Farrokh Janabi-Sharifi

doi:10.1117/12.580714

25 October 2004 Using scale-invariant feature points in visual servoing

Proceedings Volume 5603, Machine Vision and its Optomechatronic Applications; (2004) https://doi.org/10.1117/12.580714
Event: Optics East, 2004, Philadelphia, Pennsylvania, United States

Abstract

In this paper, we focus on the robust feature selection and investigate the application of scale-invariant feature transform (SIFT) in robotic visual servoing (RVS). We consider a camera mounted onto the endpoint of an anthropomorphic manipulator (eye-in-hand configuration). The objective of such RVS system is to control the pose of the camera so that a desired relative pose between the camera and the object of interest is maintained. It is seen that the SIFT feature point correspondences are not unique and hence those feature points with more than a unique match are disregarded. When the endpoint moves along a trajectory, the robust SIFT feature points are found and then for a similar trajectory the same selected feature points are used to keep track of the object in the current view. The point correspondences of the remaining robust feature points would provide the epipolar geometry of the two scenes, where knowing the camera calibration the motion of the camera is retrieved. The robot joint angle vector is then determined solving the inverse kinematics of the manipulator. We show how to select a set of robust features that are appropriate for the task of visual servoing. Robust SIFT feature points are scale and rotation invariant and effective when the current position of the endpoint is farther than and rotated with respect to the desired position.

Citation Download Citation

Azad Shademan and Farrokh Janabi-Sharifi "Using scale-invariant feature points in visual servoing", Proc. SPIE 5603, Machine Vision and its Optomechatronic Applications, (25 October 2004); https://doi.org/10.1117/12.580714

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