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Visual Servo Control Tutorial Part 1: Basic Approaches

Visual Servo Control Tutorial Part 1: Basic Approaches. Chayatat Ratanasawanya December 2, 2009 Ref: Article by Francois Chaumette & Seth Hutchinson. Overview. Introduction Basic components of visual servoing Image-based visual servo (IBVS) Position-based visual servo (PBVS)

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Visual Servo Control Tutorial Part 1: Basic Approaches

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  1. Visual Servo Control TutorialPart 1: Basic Approaches ChayatatRatanasawanya December 2, 2009 Ref: Article by Francois Chaumette & Seth Hutchinson

  2. Overview • Introduction • Basic components of visual servoing • Image-based visual servo (IBVS) • Position-based visual servo (PBVS) • Stability analysis • Conclusion • Questions/comments

  3. Introduction • Visual servo (VS) control – the use of computer vision data to control the motion of a robot. • Relies on techniques from image processing, computer vision, and control theory • Two camera configurations: • Eye-in-hand: camera is mounted on a robot manipulator or on a mobile robot. • Camera is fixed in the workspace

  4. Basic components of VS • Error function • The goal is to minimize the error • Design of s: • Consists of a set of features that are readily available in the image data (IBVS), or • Consists of a set of 3D parameters, which must be estimated from image measurements (PBVS)

  5. Basic components of VS (Cont’d) • Interaction matrix (feature Jacobian) • Design of the controller • Can be done quite simply once s is selected • The most straightforward approach is to design a velocity controller In practice, it is impossible to know perfectly Le or Le+

  6. Image-based visual servo (IBVS) • The classical IBVS schemes use the image-plane coordinates of a set of points to define s. • m - the pixel coordinates of a set of image points. • a - the camera intrinsic parameters. • For a 3D point X=(x,y,z) in the camera frame, using the projection model the point is at x=(u,v) in the image, the interaction matrix is

  7. Image-based visual servo (IBVS) • To control the 6DOF, at least three points are necessary. • If the feature vector is chosen as x=(x1,x2,x3), the Jacobian matrix can be • However, more than 3 points are usually considered because there will exist cases for which Lx is singular. Moreover, it is not possible to differentiate the global minima (poses for which e=0) when they exist.

  8. IBVS: Estimating the interaction matrix • If L e is known; i.e., if the current z of each point is available • L e is unknown, but the desired z is available • Same condition as in 1.

  9. Example of IBVS positioning

  10. IBVS result: case 1

  11. IBVS result: case 2

  12. IBVS result: case 3

  13. IBVS with a Stereovision system • A straightforward extension of the IBVS approach. • If a 3D point is visible in both left and right images, it is possible to use as visual features. • The 3D coordinates of any point observed in both images can be estimated easily by a triangulation process, it is therefore possible and quite natural to use these 3D coordinates in the features set s.

  14. Position-based visual servo (PBVS) • PBVS schemes use the pose of the camera w.r.t. some reference coordinate frame to define s. • Computing that pose from a set of measurements in an image requires the camera intrinsic parameters and the 3D model of the object observed. • m - the pixel coordinates of a set of image points. • a - the camera intrinsic parameters and the 3D model of the object.

  15. PBVS: definition of s s=(t, θu) • If t is defined relative to the object frame, we have Following the developments presented: determining Le and the estimate of its inverse, the control law is

  16. PBVS result: case 1

  17. PBVS: definition of s s=(t, θu) • If t is defined w.r.t. the current camera frame, we have the corresponding control law is

  18. PBVS result: case 2

  19. Stability analysis - IBVS • Local asymptotic stability (vc=0 and e≠e*) can be ensured when number of visual feature in the vector s is greater than 6. • Global asymptotic stability cannot be guaranteed.

  20. Stability analysis - PBVS • Global stability is achievable when all pose parameters are perfect. • Robustness: small points position computation errors in the image can lead to pose errors that may impact the accuracy and the stability of the system significantly.

  21. Conclusion • IBVS or PBVS is better? – performance tradeoffs • Stability: no strategy provides perfect properties • Correct estimation of 3D parameters is important for IBVS, but crucial for PBVS. • In PBVS, the vision sensor is considered as a 3D sensor, which leads to errors. • In IBVS, the vision sensor is considered as a 2D sensor; therefore, it is robust to errors in calibration and image noise.

  22. Questions/comments

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