代写ELEC0144、Matlab编程语言代写

日期：2022-10-10 09:40

Machine Learning for Robotics

Assignment 1

ELEC0144 – 2022/2023

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Guidelines:

? All deadlines are specified in Moodle, under the assessment section. Penalties

will be applied for late submissions in accordance with the guidelines:

https://www.ucl.ac.uk/academic-manual/chapters/chapter-4-assessmentframework-taught-programmes/section-3-module-assessment#3.12

? Please also be aware of UCL’s Academic Misconduct policy:

https://www.ucl.ac.uk/academic-manual/chapters/chapter-6-student-caseworkframework/section-9-student-academic-misconduct-procedure. Collaboration

with other teams via exchange of ideas, sharing of codes, re-using portions of the

reports etc. are not allowed and will be considered as collusion.

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1 Assignment 1: Camera and Robot Calibration

1.1 Objective Summary

In this assignment, you are required to write Matlab codes for camera calibration, robot

calibration, image processing, and relating 2D pixel position to 3D position.

1.2 Task 1: Camera Calibration Given Simulated Checkerboard Data

In this task, you will write a code to calibrate a camera given some checkerboard data.

The checkerboard pattern was downloaded from Matlab (“checkerboardPattern.pdf”),

and was then printed on an A4 paper. The size of the checkerboard square was

measured and determined to be 22mm (Figure 1). This is an important information as it

will provide the position of the checkerboard points in World frame – The points would

be automatically [0, 0], [22, 0], [44, 0], …, [0, 22], [22, 22], [44, 22], so on and so forth.

Figure 1: Size of checkerboard square [Matlab2020A]

Six images of the checkerboard are then taken using the camera, with the checkerboard

held at six different positions and orientations. The files are “Image1.png” to

“Image6.png”, and are also shown here for your reference (Figure 2).

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Figure 2: Examples of images taken by webcam.

Next, a code is written to extract the data points (Code 1). Remember, we need two

different types of data points: 1) the corner points in World frame for a single printout,

and 2) the corner points in pixel frame for 6 images.

Code 1: Code for data point extraction.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% This code is to extract the checkerboad points %

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

clear all

close all

clc

% General Settings

numImages = 6; % depends on the number of images you have

squareSize = 22; % in millimeters

% Read files

files = cell(1, numImages);

for i = 1:numImages

files{i} = fullfile(sprintf('Image%d.png', i));

end

% Display the image at working position

I = imread(files{1}); % assume image1 is the working position

figure; imshow(I);

title('Image at Working Position');

% Detect the checkerboard corners in the images.

[imagePoints, boardSize] = detectCheckerboardPoints(files);

% Generate the world coordinates of the checkerboard corners in the

% pattern-centric coordinate system, with the upper-left corner at

(0,0).

worldPoints = generateCheckerboardPoints(boardSize, squareSize);

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If you run the code, the imagePoints and worldPoints will be generated in the Matlab

workspace. They will be in the form of [x, y] coordinates.

Your task now: Using the extracted data, write a code to calibrate the camera. You

should provide one set of intrinsic parameters as a K-matrix, as well as 6 sets of extrinsic

parameters which should include position and orientation.

Further task: Next, add the following code (Code 2) to your code. The code performs

camera calibration using Matlab’s toolbox.

Code 2: Matlab’s Camera Calibration Code

% Calibrate the camera using Matlab’s toolbox.

imageSize = [size(I, 1), size(I, 2)];

cameraParams = estimateCameraParameters(imagePoints, worldPoints, ...

'ImageSize', imageSize);

Run the code and see the results of “cameraParams” in Matlab workspace, then

compare and discuss the differences between your own results and the toolbox’s

results. Why are there differences? You might want to refer to Zhengyou Zhang’s paper

(Zhang2000) for some details.

Task 1: Write your own code to obtain the intrinsic and extrinsic parameters of the

camera, given checkerboard dataset. Then compare your results with Matlab

toolbox’s results.

What to write in your report:

? Details of the algorithm, derivation, results, discussions and comparisons.

Additional files to submit:

? Your Matlab code (m file) named “Vision_Task1.m”, which will be test run by

your module leader / tutors. The code should be properly commented.

Additionally: Save your intrinsic parameter matrix as “K.mat”, and the extrinsic

parameters of the first image, [??1 ??2 ??3 ??????????

??

] as “Tcw.mat” (cw stands for

camera-to-world), which you will need to use for Task 4 later.

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1.3 Task 2: Robot Calibration Given Simulated TCP Data

In this task, you will write a code to calibrate the robot, i.e. to find the position and

orientation of the world frame with respect to the robot.

Six points on the checkerboard have been selected. Their positions according to the

World coordinate system (on the checkerboard itself) are:

Point 1 2 3 4 5 6

????

-22 0 22 44 66 154

????

-22 22 66 22 88 -22

The same six points have been “probed” by the robot using a sharp-tipped tool. The

positions according to the Robot coordinate system are:

Point 1 2 3 4 5 6

?????? -121.6 -100.4 -79.16 -56.39 -35.55 54.32

?????? 222.4 178 133.6 177.2 110.9 219.3

?????? -20 -20.04 -20.08 -19.96 -20.04 -19.7

Task 2: Write your own code to find the position and orientation of the World frame

with respect to the robot frame, given the positions of the checkerboard corner

points.

What to write in your report:

? Details of the algorithm, derivation, results and discussions.

Additional files to submit:

? Your Matlab code (m file) named “Vision_Task2.m”, which will be test run by

your module leader / tutors. The code should be properly commented.

Additionally: Save your result of position and orientation of world frame with respect to

robot frame as a single homogeneous transformation matrix named “Trw.mat” (rw

stands for robot-to-world), which you will need to use for Task 4 later.

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1.4 Task 3: Image Processing given Simulated Image

In the “Task3Data” folder, you will find an image of “rectangular” object named

“Object.png”. It is of size 640x480 (Figure 3).

Figure 3: Camera view

Your task is to use image processing methods discussed in the lecture to determine the

position, orientation, circularity, edges and corners of the object.

Task 3: Write your own code to find the position, orientation, circularity, edges and

corners of the object.

What to write in your report:

? Details of the algorithm, derivation, results and discussions.

Additional files to submit:

? Your Matlab code (m file) named “Vision_Task3.m”, which will be test run by

your module leader / tutors. The code should be properly commented.

Additionally: Save your result of x and y positions of the object, in the form of

[?? ?? 0]

??

, as “PObject.mat”, which you will need to use for Task 4 later.

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1.5 Task 4: Relating Pixel Position to Robot Position using Simulated

Data

Before you start with task 4, please ensure that you have the following mat files from

earlier tasks:

? K.mat from Task 1

? Tcw.mat from Task 1

? Trw.mat from Task 2

? PObject.mat from Task 3

The data in PObject.mat are 2D positions of the object with respect to the camera pixel

frame. Now, use K.mat, Tcw.mat and Trw.mat to find the actual 3D positions of the

object with respect to the robot frame.

Task 4: Find the 3D positions of object with respect to robot frame.

What to write in your report:

? Details of the algorithm, derivation, results and discussions.

Additional files to submit:

? Your Matlab code (m file) named “Vision_Task4.m”, which will be test run by

your module leader / tutors. The code should be properly commented.

? K.mat, Tcw.mat, Trw.mat, PObject.mat

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1.6 What to Submit

? Your Matlab codes, with proper comments. The code will be tested!

? A written report which details the implementation of your calibration algorithm

and image processing, results, comparisons, discussions etc.

? You should put everything (Matlab codes AND your written report) into a zip

folder, then submit the zip folder onto the submission point on Moodle. Note:

please do not submit .rar file – only .zip is allowed.

1.7 More about the Report

The report should have a cover page clearly indicating the following details:

? Report title.

? Team/Student name.

? Full name, student number and email address for each team member.

? Submission date.

The body of the report must be organized under the following section headings:

? Executive summary

? Task 1: Camera Calibration Given Simulated Checkerboard Data

? Task 2: Robot Calibration Given Simulated TCP Data

? Task 3: Image Processing given Simulated Image

? Task 4: Relating Pixel Position to Robot Position using Simulated Data

? Conclusion

The list of references should appear on separate pages. References should be formatted

using the IEEE Citation Style. It is extremely important that all third party sources of

information are properly credited and referenced in the correct manner. The inclusion

of any text or diagrams from websites or documents must be clearly indicated and

referenced.

Font size should be exactly 11 point. Recommended font type is Calibri or Arial. Text

should be both left and right aligned (justified text). All figures should have captions, axes

labels and legends where appropriate. Curves should be distinguishable even if printed in

black and white.

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1.8 Marking Criteria

This assignment contributes 20% to the overall score of the module. The marking

criteria are described in the following table:

Criteria Mark

Weight

Task 1 code Code works properly, with good comments. 4%

Task 2 code Code works properly, with good comments. 3%

Task 3 code Code works properly, with good comments. 3%

Task 4 code Code works properly, with good comments. 2%

Report (Details) Details of the algorithm, derivation, discussions and

comparisons clear and comprehensive.

6%

Report (Format) English syntax and style, general organization and

formatting, figure, table and equation presentation

and use, literature citations are use all appropriate.

2%

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2 List of References

Matlab2020A, Single Camera Calibration App, accessed 1 May 2020,

<https://uk.mathworks.com/help/vision/ug/single-camera-calibrator-app.html>.

Zhang2000, Zhengyou Zhang, “A Flexible New Technique for Camera Calibration,” IEEE

Transactions on Pattern Analysis and Machine Intelligence, Volume 22, Issue 11,

November 2000.