Visual Servoing and Target Tracking

transcript

CH24 in Robotics Handbook

Presented by Wen LiPh.D. student

Texas A&M University

Outline

Visual Servo Control Image based visual servo Position based visual servo Hybrid visual servo and other issues Target Tracking

Outline

Visual Servo Control Image based visual servo Position based visual servo Hybrid visual servo and other issues Target Tracking

Visual Servo Control Vision Based Robot

Control

Task: USE - computer vision data CONTROL - motion of a robot

Visual Servo Control

Camera Configuration:Eye-in-handFixed in workspace

Servoing Architecture

control law

Data extraction

Basic Components Image features Error function Velocity controller Interaction matrix

Basic Components Image features Error function Velocity controller Interaction matrixs(m(t),a) ; a is a set of parameters that represent potential additional knowledge about the system (e.g. Camera intrinsic parameters); m(t) is a set of image measurements (e.g. Image coordinates of interest points)s* contains the desired values of the features.

e(t)=s(m(t),a)-s*The aim of the control scheme is to minimize error e(t)At the desired pose, e(t)=0.

The control law

vc – the spatial velocity of the camera, input to the robot controller Problem: what is the form of Ls

Basic Components Image features Error function Velocity controller Interaction matrixLs is the interaction matrix, which describes the relationship between the time variation of s and the camera velocity vc. , Le=Ls is the approximation of the pseudo-inverse of Ls.Problem: how to estimate -- according to different designs of s

Categories: Image based control Position based control

Outline

Visual Servo Control Image based servo control Position based servo control Hybrid visual servo and other issues Target Tracking

Image Based Visual Servo (IBVS)

S(m(t),a)

Image Based Visual Servo (IBVS)

Image Based Visual Servo (IBVS) Image features s(m(t),a)

Traditionally, s is defined by the image-plane coordinates of a set of points. s=x=(x,y)

Image Based Visual Servo (IBVS) Interaction Matrix

The value Z is the depth of the point relative to the camera frame. Therefore, any control scheme that uses this form of the interaction matrix must estimate or approximate the value of Z.When Z is not known, cannot be directly used. An approximation must be used.

To control six degrees of freedom, at least three points are necessary. There exists some configurations for which Lx is singular.

Image Based Visual Servo (IBVS) Effects of different estimations of Ls

Image Based Visual Servo (IBVS) Advantages:

The positioning accuracy of the system is less sensitive to camera calibration errors

Computational advantageDisadvantages:

Presence of singularityServoing in 2-D

Outline

Position Based Visual Servo (PBVS)

extract the image features -> compute the current camera pose with respect to

a reference coordinate on the object -> compare with the desired camera pose with

respect to the reference coordinate on the objectCurrent pose

desired pose

Position Based Visual Servo (PBVS) Consider three coordinate frames:

The current camera frame The desired camera frame A reference frame attached to the object gives the coordinates of the origin of the

object frame to the current camera frame gives the coordinates of the origin of the

object frame to the desired camera frame , the rotation matrix that gives the

orientation of the current camera frame relative to the desired frame

Current pose

desired pose

Position Based Visual Servo (PBVS) Define s=(t,θu)

t is a translation vector, θu is the angle/axis parameterization for the rotation

1) t is defined relative to the object frame

Position Based Visual Servo (PBVS) Define s=(t,θu)

t is a translation vector, θu is the angle/axis parameterization for the rotation

2) t is defined relative to the desired camera frame

Effects of different designs

Position Based Visual Servo (PBVS) Advantages:

Possible to describe tasks in terms Cartesian pose as is common in Robotics

Disadvantages: Sensitive to calibration error Depend on having an accurate mode of

target objects – a form of calibrationsServoing in 3-D

Outline

Visual Servo Control Image based servo control Position based servo control Hybrid servo and other issues Target Tracking

Hybrid servo and other extensions Hybrid VS – combining 2-D and 3-D

features 2.5-D visual servo – add depth of the

s▪ Camera trajectory is a straight line▪ Image trajectory of the center of the gravity

of the object is also a straight line

Hybrid servo and other issues Stereo vision system in IBVS

Because of epipolar constraint, this approach actually requires 3-D parameters in s. Thus, it would be, strictly speaking, a position-based approach

Outline

Target Tracking

Moving target => varying value s*(t)

The time variation of e due to the generally unknown target motion

Estimate ∂e/∂t Improve estimated value using Kalman filter or more-elaborate filtering methods

Thanks!

Visual Servoing and Target Tracking

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