Camera Laser Calibration - Robotics & Computer Vision Laboratory

Report
Understanding Laser Sensor
and its Calibration with Camera
Youngjin Yoon / Yongseop Jeong
Apr. 16, 2012
Robotics and Computer Vision Laboratory
KAIST
System Configuration
Camera
Laser
Camera
Laser
Laser Range Finder
• Device which uses a
laser beam to
determine the
distance to objects
Laser Range Finder: LMS151
• Specifications
Using 905nm IR
Field of view: 270º
50Hz Scanning Freq.
0.5º angular
resolution
– Operating Range:
0~50m
– Max. range with 10%
reflectivity: 18m
–
–
–
–
Sample Laser Scanner Data
Pattern
Laser
For Left camera, you need only left pattern
Objectives
[ | ]


[ | ]
Camera 2

Camera 1
[ | ]
Laser Scanner
To estimate   , you should find  first. [1,2]
Find  then estimate   (optional)
What is Camera Calibration?
• Estimating extrinsic relation between 3D
world and 2D camera and the camera
internal parameter
1) Intrinsic parameter K
• Optical/geometrical property of a camera
2) Extrinsic Parameter [R|t]
• Rotation and translation matrix
Camera Calibration[1]
• Estimate Homography
• Intrinsic parameter estimation (K)
• Extrinsic parameter estimation ([R|t])
• Stereo Calibration(Optional)
Recommended
Experiment Schedule
• Week 1, 4/16(Mon.) – 4/22(Sun.)
– Camera intrinsic parameter estimation
• 
– Extrinsic parameter of Camera 1
• [ | ]
• [ | ] is defined in world coordinates
– Note that each image derives each [ | ]
– But  is unique.
Recommended
Experiment Schedule
• Week 2, 4/23(Mon.) – 4/29(Sun.)
– Extrinsic parameter of Laser
•   : Relative pose of the laser range finder
w.r.t. the Camera 1
Recommended
Experiment Schedule
• Extra work
–  and  
• Non-overwrapping stereo calibration
•   is the relative pose w.r.t. the Camera1
• You’ll receive super-extra points
Recommended
Experiment Schedule
• You don’t have to submit 2 reports
– Just submit the result of 2 weeks until the
due date.
Given data
• Pattern image
– Pattern.zip
– Includes pictures of patterns from camera 1
and 2
• Laser Data
– Laser.zip
Extra Points
• Compute K, R and t without toolbox
• Automatic feature extraction from
pattern
– Get each pixel point automatically
• Optimization
• Non-overwrapping stereo calibration
– Extra work
Submission
• Due: 23:59:59, April 29(Sun.), 2012
• Youngjin Yoon ([email protected])
– Title: [RE510] ID - NAME
• Example: [RE510] 20113573 – Lee Myungbak
– Attachment file
• Report (doc, docx, hwp or pdf)
• Executable File
• Source Code(compile-able)
– All used libraries must be included in submitted
package
Submission: Report
– Report should contain
• The answers;  , [ | ],  , [ | ]
– Justify your answer with any means
» Calculating re-projection error or project laser ray on image
sets
•
•
•
•
Your progress to estimate them
List of used toolbox(es) and libraries
Referred resources(papers, books, etc.)
Developing Environment
– Version of OS, MSVS, MATLAB, …
– Don’t attach whole source code in your report!
• Detailed explanation and comment on source code is
enough
– Language: Korean, English
Submission: Executable File
• Remarks
– Compiled(executable) file should be
attached in a separate folder
• With required dll, lib, data files and etc.
• If you used MATLAB, m-files are OK
– With a execution manual
• If your program is not executable, you’ll lose a
lot of points
• Please check your program on another computer
Submission: Source Code
• Remarks
– PLEASE add comments
– Include all libraries you used
• Your project should be compiled on our
computer
Grading Policy
Item
Maximum Points
Understanding
40
Accuracy of the answers
20
Justifying answers(includes logical sense)
20
Completion of program
20
Extra Points
Extra works(Non-overwrapping stereo calibration)
20
Without toolboxes
10
Automatic feature extraction
10
Optimization
10
Reference
[1] Flexible Camera Calibration By Viewing a Plane From Unknown
Orientations
–
Zhengyou Zhang, ICCV 1999
[2] Extrinsic Calibration of a Camera and Laser Range Finder
(improves camera calibration)
–
Qilong Zhang, Robert Pless, IROS 2004
[3] Multiple View Geometry (2/E)
–
Hartley, Zisserman

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