3D Motion Determination Using µIMU And Visual Tracking

14 May 2010

Centre for Micro and Nano SystemsThe Chinese University of Hong Kong

Supervised by Prof. LiLam Kin Kwok, Mark

Outline

• Brief summary of previous works

• Detail of Visual Tracking System (VTS)- Perspective Camera Model- Procedure of Pose Estimation

• Current Results of VTS

• Conclusion

• Future Plan

Previous Works

• Implement Harris Corner Finding Algorithm- Automatic finding good features

• Improve the performance of LK Tracking Method- Reduce the noise generated by inconstant lighting

• Find some information about high speed camera (>60fps)

Previous Works

Detail of Visual Tracking System

Select ROI fromcaptured image

Extract Good Features(Harris Algorithm)

Motion Tracking(LK Tracking Method)Pose Estimation

Position and Orientation(Camera Coordinate)

CoordinateTransformation

Final Pose of Camera(World Coordinate)

Perspective Camera Model

Optical Axis

Square Grid

Image Plan

f

li

P1

P3

P2

P4

p1p2

p3 p4

C

C: Optical Centerf : Focal Lengthli : Distance between 3D feature points and the optical centerPi : 3D Feature Points on the square gridpi : Corresponding 2D projected image points

cy

cx

cz

{ C }

wy

wx

wz

{ W }

{ W } : World Coordinate{ I } : Image Coordinate{ C } : Camera Coordinate

v

u{ I }

Perspective Camera Model

• Relationship between image point and 3D scene point

Image Plan

Optical Axis

Scene

Optical Center

f

cZ

cx

cX

z

x{ C }

Pose Estimation ProcedureCalibrate camera

(obtain interior parameter)Target Dimension

Target ImageStep 1:Calibration and Measurement

Calculate distancebetween

target and camera

Step 2:Recover Pose of Camera( Respect to Camera Coordinate)

CalculateTransformation Matrix

Step 3:Recover Transformation Matrixbetween Camera to World Coordinate

Final PoseStep 4:Transform the coordinate toWorld Coordinate

Pose Estimation (Step 1)

• Using square pattern (with known dimensions) to calibrate a camera

Pose Estimation (Step 2)

• Image to Camera Coordinate Transformation

Image Coordinate:

Camera Coordinate:

Image Plan

f

p1p2

p3 p4

C

cy

cx

cz

{ C }

v

u{ I }

(uo , vo) is image principal point

Optical Axis

• Areas of triangles (Given):

• Volumes of tetrahedra:

• Use unit vector cui to represent cPi

Pose Estimation (Step 2)

C

cy

cx

cz

{ C }

P1P3

P2

P4

hcP1

cP2cP4

cP3

cui

(From Step 1)

Pose Estimation (Step 2)

• Use vectors to calculate Volume:

• Express d2, d3, d4 as a function of d1:

C

cy

cx

cz

{ C }

P1P3

P2

P4

hcP1

cP2cP4

cP3

cui

Pose Estimation (Step 2)

• Use a line segment s1k to compute squared distance:

• Use parametric representation and simplify

C

cy

cx

cz

{ C }

P1

P2

cP1

cP2

cu1 cu2

s12

Pose Estimation (Step 2)

• Substitute d1 into the following equation to obtain the 3D coordinates of the feature points:

• Transformation Matrix wTo is given• Transformation matrix oTc can be obtained by step 2

Pose Estimation (Step 3)

cycx

cz

{ C }

wy

wx

wz{ W }

oy

ox

oz { O }

wTo

oTc

{ W } : World Coordinate{ O } : Object Coordinate{ C } : Camera Coordinate

Pose Estimation (Step 4)

• The Final Pose of camera can be solved

cycx

cz

{ C }

wy

wx

wz{ W }

oy

ox

oz { O }

wTo

oTc

wTc

Current Results of VTS

• Experimental Setup

Motion RecordingComputer

Webcam

Feature

Ruler

Current Results of VTS

Conclusion

• Heavily depend on image points- Increase image resolution (Now using 640 X 480 pixels)

• Use some optimization methods to increase accuracy- Gauss-Newton Line search method

Future Plan

• Develop this method and test the performance

• Try to fuse the data with the µIMU data

• Develop the optimization method after finishing data fusion

Reference[1] Abidi M.A. , Chandra T., “A new efficient and direct solution for estimation using quadrangular

targets: algorithm and evaluation,” IEEE transactions on pattern analysis and machine intelligence, Vol.17, No.5, pp.534-538, 1995.

[2] Abidi M.A. , Chandra T., “Pose estimation for camera calibration and landmark tracking,” IEEE International Conference on Robotics and Automation, 2009.

[3] Forsyth Ponce, “Computer Vision: A Modern Approach,” Prentice Hall, 2003

Thanks for your attention