Camera calibration❖ Pinhole Camera Model... Camera calibration❖ Pinhole Camera Model ▪ The calibration algorithm calculates the camera matrix usin ➢ The intrinsic parameters ➢ The extr
Trang 1XỬ LÝ ẢNH TRONG CƠ ĐIỆN
Machine Vision
TRƯỜNG ĐẠI HỌC BÁCH KHOA
Giảng viên: TS Nguyễn Thành Hùn
Đơn vị : Bộ môn Cơ điện tử , Viện Cơ
Hà Nội, 2021
Trang 2Chapter 9 Camera Calibration and 3D R
1 Camera calibration
2 Robot Camera Calibration
3 Pose estimation
4 Stereo vision
Trang 3Chapter 9 Camera Calibration and 3D R
1 Camera calibration
2 Robot Camera Calibration
3 Pose estimation
4 Stereo vision
Trang 5Camera calibration
Trang 6❖ Camera parameters include intrinsics, extrinsics, and distor
Trang 9Camera calibration
❖ Pinhole Camera Model
Trang 10Camera calibration
❖ Pinhole Camera Model
▪ The calibration algorithm calculates the camera matrix usin
➢ The intrinsic parameters
➢ The extrinsic parameters
Scale factor Image points Intrinsic
matrix
Extrinsics Rotation and translation
Trang 12Camera calibration
❖ Camera Calibration Parameters
Trang 14Camera calibration
❖ Intrinsic Parameters
➢ The intrinsic parameters include the focal length, the optica
principal point, and the skew coefficient The camera intrinsic m
➢ The pixel skew defined :is as
Trang 15Camera calibration
❖ Intrinsic Parameters
Trang 16not have a lens To accurately represent a real camera, the cam
and tangential lens distortion
Trang 18
•x y, — Undistorted pixel locations and are in normalized imagex yimage coordinates are calculated from pixel coordinates by translatindividing by the focal length in pixels Thus, and are dimensionlesx y
•k1, k2, and k3 — Radial distortion coefficients of the lens.
•r2: x2 + y2
Trang 19Camera calibration
❖ Tangential Distortion
➢ Tangential distortion occurs when the lens and the image p
tangential distortion coefficients model this type of distortion
Trang 20•p1 and p2 — Tangential distortion coefficients of the lens.
•r2: x2 + y2
Trang 21Camera calibration
Image plane
Trang 22Camera Calibration with Op
❖ Function calibrateCamera use the above model to do the
➢ Project 3D points the image plane given intrinsic and extrinsito
➢ Compute extrinsic parameters given intrinsic parameters, aprojections
➢ Estimate intrinsic and extrinsic camera parameters from several
Trang 23Chapter 9 Camera Calibration and 3D R
1 Camera calibration
2 Robot Camera Calibration
3 Pose estimation
4 Stereo vision
Trang 24Robot Camera Calibrati
Eye To Hand
Camera fixed to an independent structure Cam
Trang 25Robot Camera Calibrati
Ai: transformation from Base → Gripper
Bi: transformation from Target → Cam
X: transformation from Gripper → Target???
Trang 26Robot Camera Calibrati
Ai: transformation from Base → Gripper
Bi: transformation from Target → Cam
X: transformation from Gripper → Target???
Trang 27Robot Camera Calibrati
Ai: transformation from Base → Gripper
Bi: transformation from Cam → Target
X: transformation from Gripper → Cam???
Trang 28Robot Camera Calibrati
➢ https://github.com/jhu-lcsr/handeye_calib_camodocal
➢ https://github.com/hengli/camodocal
➢ https://visp-doc.inria.fr/doxygen/visp-daily/classvpHandEyeCalibration.html#a68ab6
➢ https://github.com/zhixy/SolveAXXB
Trang 30Camera Pose Estimatio
Trang 31Camera Pose Estimatio
points on the 3D model curve
(a) Reconstruct projection rays from the image points
(b) Estimate the nearest point of each projection ray to a point on(c) Estimate the pose of the contour with the use of this correspo(d) goto (b)
Trang 32Camera Pose Estimation with
Trang 37Stereo Vision
❖ What epipolar geometry? is
Trang 38Stereo Vision
❖ What epipolar geometry? is
Epipolar lines Epipolar lines
Trang 40Stereo Vision
❖ What epipolar geometry? is
Trang 42Stereo Vision
❖ Building the 3D map
Trang 45Stereo Vision
❖ Building the 3D map