Reproduce pitch from Euler angles using Quaternions in MATLAB - matlab

I have a followup question from this post : Extracting Yaw from a Quaternion .
Alike the OP, I want to move away from Euler rotations and use Quaternions. My scenario is as follows:
I am wearing an Apple smartwatch on my left hand and performing the bicep curls exercise. I do a few reps in the first set, and stop. I then rotate my body 90 degrees to the right in my room, and do another set of a few repetitions. I do this 4 times, and store the quaternions to disk using the xArbitraryCorrectedZVertical frame. When I use Euler rotations to describe pitch, using below code, I see a sinusoidal shape. When I want to reproduce the same pattern using quaternions, I get close, except the signal drifts. Here is my code:
clc;
clear;
quaternions_table = readtable(strcat("quaternions"), 'Delimiter',',');
attitude = quaternion(quaternions_table.w, quaternions_table.x, quaternions_table.y, quaternions_table.z);
quats = zeros(length(attitude), 1);
eulers = zeros(length(attitude), 1);
for i = 1:length(attitude)
[w,x,y,z] = parts(attitude(i) * quaternion(0,0,0,1));
quats(i) = rad2deg(atan2(z,w));
[w,x,y,z] = parts(attitude(i));
eulers(i) = rad2deg(asin(2*(y * w - z * x)));
end
figure(1);
clf;
hold on;
plot(quats, '.r');
plot(eulers, '.b');
legend('Q', 'E');
hold off;
Here is a plot of the difference:
How can I fix my code so that the quaternion generated pattern is equivalent to the euler's pitch, regardless of my "body" rotation?
Some sample data: https://filebin.net/02k2323ccbct1n38/quaternions

DISCLAIMER 1: this is a first attempt at putting together an answer. What follows is by no means conclusive. Any feedback aimed at improving this answer is sincerely welcome.
DISCLAIMER 2: I don't have access to any toolboxes featuring functions related to quaternions, so I relied on what's available on Mathworks's File Exchange, to which I added a function that builds quaternions by extracting their components from a table. Using the quaternion-related functions provided in Matlab's toolboxes, the OP should be able to replicate the calculations showcased hereinafter.
DISCLAIMER 3: the selection of the point to which the rotations are applied matters. Picking a different P will affect the resulting plot. In particular, with points far from P(1,0,0) the 3D map of the successive positions in space doesn't appear to be compatible with the physics of the OP moving the smart-watch in the way described in the question.
With the sample data provided by the OP, I was able to apply the quaternions to the point with coordinates P(1,0,0) i.e. a point on the global x-axis, using (pseudo-code):
% Pseudo code
rotatedP = Qi * P * conjugate(Qi);
where Qi is the i-th quaternion extracted from the sample data.
Here's a plot showing the positions in space assumed by the point after being rotated:
Once the coordinates of the point are available, calculating the pitch could be done by computing the acos of the dot product between the unit vector parallel to OP (O being the global origin) and the unit vector parallel to the z-axis (with components (0,0,1)), in pseudo-code:
% Pseudo code
pitch = acos(OP.z/norm(OP))
Here's a plot of the pitch shifted using the first value of the array, which is extremely close to what was obtained by the OP using Euler's angles:
Matlab code
Here's the code I used to generate the plots. As mentioned erlier, I relied on Przemyslaw Baranski's Quaternions. Please, note that my code is a very crude attempt at tackling the problem, with basically no optimization. Uncommenting the lines for the 3D plot slows down the process considerably:
function draw_quats()
% point to be rotated
P = [ 1, 0, 0 ];
% Import quaternion components
q_tab = readtable(strcat("quaternions_data"), 'Delimiter',',');
pitch = [];
pp = 1;
for k = 1:1:numel(q_tab.w)
% create a quaternion for rotation
Qrot = qGetQuaternionFromComponents(q_tab.x(k), q_tab.y(k), q_tab.z(k), q_tab.w(k));
% rotate point
Prot = qRotatePoint( P, Qrot );
Pnorm = sqrt(Prot(1)^2 + Prot(2)^2 + Prot(3)^2);
% % display axes
% quiver3( 0,0,0, 1,0,0 );
% hold on;
% quiver3( 0,0,0, 0,1,0 );
% quiver3( 0,0,0, 0,0,1 );
%
% % display point
% plot3( Prot(1), Prot(2), Prot(3), 'b.' );
%
% % setup the plot
% grid on;
% axis equal;
% axis([-1 1.0 -1 1.0 -1 1.0]);
% xlabel( 'x' );
% ylabel( 'y' );
% zlabel( 'z' );
%
% view( 45, 20 );
% drawnow;
% Compute pitch
pitch(pp) = acos(Prot(3)/Pnorm)*180/pi;
pp = pp + 1;
end
figure
plot(pitch-pitch(1),'.k');
end
And this is the definition of qGetQuaternionFromComponents:
function Q = qGetQuaternionFromComponents( x, y, z, w )
Q = [w x y z]';
end
In the event you have some of the toolboxes by MATLAB, you can do something like this:
for i = 1:length(attitude)
qPoint = quaternion(0,-1,0,0);
q = attitude(i);
[~,x,y,z] = parts(q * qPoint * conj(q));
Pnorm = sqrt(x^2 + y^2 + z^2);
quats(i) = asind(z/Pnorm);
end
References
Euclideanspace.com
Mathworks' FileExchange

Related

How can I reduce the number of mesh lines shown in a surface plot?

I've found this answer, but I can't complete my work. I wanted to plot more precisely the functions I am studying, without overcoloring my function with black ink... meaning reducing the number of mesh lines. I precise that the functions are complex.
I tried to add to my already existing code the work written at the link above.
This is what I've done:
r = (0:0.35:15)'; % create a matrix of complex inputs
theta = pi*(-2:0.04:2);
z = r*exp(1i*theta);
w = z.^2;
figure('Name','Graphique complexe','units','normalized','outerposition',[0.08 0.1 0.8 0.55]);
s = surf(real(z),imag(z),imag(w),real(w)); % visualize the complex function using surf
s.EdgeColor = 'none';
x=s.XData;
y=s.YData;
z=s.ZData;
x=x(1,:);
y=y(:,1);
% Divide the lengths by the number of lines needed
xnumlines = 10; % 10 lines
ynumlines = 10; % 10 partitions
xspacing = round(length(x)/xnumlines);
yspacing = round(length(y)/ynumlines);
hold on
for i = 1:yspacing:length(y)
Y1 = y(i)*ones(size(x)); % a constant vector
Z1 = z(i,:);
plot3(x,Y1,Z1,'-k');
end
% Plotting lines in the Y-Z plane
for i = 1:xspacing:length(x)
X2 = x(i)*ones(size(y)); % a constant vector
Z2 = z(:,i);
plot3(X2,y,Z2,'-k');
end
hold off
But the problem is that the mesh is still invisible. How to fix this? Where is the problem?
And maybe, instead of drawing a grid, perhaps it is possible to draw circles and radiuses like originally on the graph?
I found an old script of mine where I did more or less what you're looking for. I adapted it to the radial plot you have here.
There are two tricks in this script:
The surface plot contains all the data, but because there is no mesh drawn, it is hard to see the details in this surface (your data is quite smooth, this is particularly true for a more bumpy surface, so I added some noise to the data to show this off). To improve the visibility, we use interpolation for the color, and add a light source.
The mesh drawn is a subsampled version of the original data. Because the original data is radial, the XData and YData properties are not a rectangular grid, and therefore one cannot just take the first row and column of these arrays. Instead, we use the full matrices, but subsample rows for drawing the circles and subsample columns for drawing the radii.
% create a matrix of complex inputs
% (similar to OP, but with more data points)
r = linspace(0,15,101).';
theta = linspace(-pi,pi,101);
z = r * exp(1i*theta);
w = z.^2;
figure, hold on
% visualize the complex function using surf
% (similar to OP, but with a little bit of noise added to Z)
s = surf(real(z),imag(z),imag(w)+5*rand(size(w)),real(w));
s.EdgeColor = 'none';
s.FaceColor = 'interp';
% get data back from figure
x = s.XData;
y = s.YData;
z = s.ZData;
% draw circles -- loop written to make sure the outer circle is drawn
for ii=size(x,1):-10:1
plot3(x(ii,:),y(ii,:),z(ii,:),'k-');
end
% draw radii
for ii=1:5:size(x,2)
plot3(x(:,ii),y(:,ii),z(:,ii),'k-');
end
% set axis properties for better 3D viewing of data
set(gca,'box','on','projection','perspective')
set(gca,'DataAspectRatio',[1,1,40])
view(-10,26)
% add lighting
h = camlight('left');
lighting gouraud
material dull
How about this approach?
[X,Y,Z] = peaks(500) ;
surf(X,Y,Z) ;
shading interp ;
colorbar
hold on
miss = 10 ; % enter the number of lines you want to miss
plot3(X(1:miss:end,1:miss:end),Y(1:miss:end,1:miss:end),Z(1:miss:end,1:miss:end),'k') ;
plot3(X(1:miss:end,1:miss:end)',Y(1:miss:end,1:miss:end)',Z(1:miss:end,1:miss:end)','k') ;

MATLAB: 3d reconstruction using eight point algorithm

I am trying to achieve 3d reconstruction from 2 images. Steps I followed are,
1. Found corresponding points between 2 images using SURF.
2. Implemented eight point algo to find "Fundamental matrix"
3. Then, I implemented triangulation.
I have got Fundamental matrix and results of triangulation till now. How do i proceed further to get 3d reconstruction? I'm confused reading all the material available on internet.
Also, This is code. Let me know if this is correct or not.
Ia=imread('1.jpg');
Ib=imread('2.jpg');
Ia=rgb2gray(Ia);
Ib=rgb2gray(Ib);
% My surf addition
% collect Interest Points from Each Image
blobs1 = detectSURFFeatures(Ia);
blobs2 = detectSURFFeatures(Ib);
figure;
imshow(Ia);
hold on;
plot(selectStrongest(blobs1, 36));
figure;
imshow(Ib);
hold on;
plot(selectStrongest(blobs2, 36));
title('Thirty strongest SURF features in I2');
[features1, validBlobs1] = extractFeatures(Ia, blobs1);
[features2, validBlobs2] = extractFeatures(Ib, blobs2);
indexPairs = matchFeatures(features1, features2);
matchedPoints1 = validBlobs1(indexPairs(:,1),:);
matchedPoints2 = validBlobs2(indexPairs(:,2),:);
figure;
showMatchedFeatures(Ia, Ib, matchedPoints1, matchedPoints2);
legend('Putatively matched points in I1', 'Putatively matched points in I2');
for i=1:matchedPoints1.Count
xa(i,:)=matchedPoints1.Location(i);
ya(i,:)=matchedPoints1.Location(i,2);
xb(i,:)=matchedPoints2.Location(i);
yb(i,:)=matchedPoints2.Location(i,2);
end
matchedPoints1.Count
figure(1) ; clf ;
imshow(cat(2, Ia, Ib)) ;
axis image off ;
hold on ;
xbb=xb+size(Ia,2);
set=[1:matchedPoints1.Count];
h = line([xa(set)' ; xbb(set)'], [ya(set)' ; yb(set)']) ;
pts1=[xa,ya];
pts2=[xb,yb];
pts11=pts1;pts11(:,3)=1;
pts11=pts11';
pts22=pts2;pts22(:,3)=1;pts22=pts22';
width=size(Ia,2);
height=size(Ib,1);
F=eightpoint(pts1,pts2,width,height);
[P1new,P2new]=compute2Pmatrix(F);
XP = triangulate(pts11, pts22,P2new);
eightpoint()
function [ F ] = eightpoint( pts1, pts2,width,height)
X = 1:width;
Y = 1:height;
[X, Y] = meshgrid(X, Y);
x0 = [mean(X(:)); mean(Y(:))];
X = X - x0(1);
Y = Y - x0(2);
denom = sqrt(mean(mean(X.^2+Y.^2)));
N = size(pts1, 1);
%Normalized data
T = sqrt(2)/denom*[1 0 -x0(1); 0 1 -x0(2); 0 0 denom/sqrt(2)];
norm_x = T*[pts1(:,1)'; pts1(:,2)'; ones(1, N)];
norm_x_ = T*[pts2(:,1)';pts2(:,2)'; ones(1, N)];
x1 = norm_x(1, :)';
y1= norm_x(2, :)';
x2 = norm_x_(1, :)';
y2 = norm_x_(2, :)';
A = [x1.*x2, y1.*x2, x2, ...
x1.*y2, y1.*y2, y2, ...
x1, y1, ones(N,1)];
% compute the SVD
[~, ~, V] = svd(A);
F = reshape(V(:,9), 3, 3)';
[FU, FS, FV] = svd(F);
FS(3,3) = 0; %rank 2 constrains
F = FU*FS*FV';
% rescale fundamental matrix
F = T' * F * T;
end
triangulate()
function [ XP ] = triangulate( pts1,pts2,P2 )
n=size(pts1,2);
X=zeros(4,n);
for i=1:n
A=[-1,0,pts1(1,i),0;
0,-1,pts1(2,i),0;
pts2(1,i)*P2(3,:)-P2(1,:);
pts2(2,i)*P2(3,:)-P2(2,:)];
[~,~,va] = svd(A);
X(:,i) = va(:,4);
end
XP(:,:,1) = [X(1,:)./X(4,:);X(2,:)./X(4,:);X(3,:)./X(4,:); X(4,:)./X(4,:)];
end
function [ P1,P2 ] = compute2Pmatrix( F )
P1=[1,0,0,0;0,1,0,0;0,0,1,0];
[~, ~, V] = svd(F');
ep = V(:,3)/V(3,3);
P2 = [skew(ep)*F,ep];
end
From a quick look, it looks correct. Some notes are as follows:
You normalized code in eightpoint() is no ideal.
It is best done on the points involved. Each set of points will have its scaling matrix. That is:
[pts1_n, T1] = normalize_pts(pts1);
[pts2_n, T2] = normalize-pts(pts2);
% ... code
% solution
F = T2' * F * T
As a side note (for efficiency) you should do
[~,~,V] = svd(A, 0);
You also want to enforce the constraint that the fundamental matrix has rank-2. After you compute F, you can do:
[U,D,v] = svd(F);
F = U * diag([D(1,1),D(2,2), 0]) * V';
In either case, normalization is not the only key to make the algorithm work. You'll want to wrap the estimation of the fundamental matrix in a robust estimation scheme like RANSAC.
Estimation problems like this are very sensitive to non Gaussian noise and outliers. If you have a small number of wrong correspondence, or points with high error, the algorithm will break.
Finally, In 'triangulate' you want to make sure that the points are not at infinity prior to the homogeneous division.
I'd recommend testing the code with 'synthetic' data. That is, generate your own camera matrices and correspondences. Feed them to the estimate routine with varying levels of noise. With zero noise, you should get an exact solution up to floating point accuracy. As you increase the noise, your estimation error increases.
In its current form, running this on real data will probably not do well unless you 'robustify' the algorithm with RANSAC, or some other robust estimator.
Good luck.
Good luck.
Which version of MATLAB do you have?
There is a function called estimateFundamentalMatrix in the Computer Vision System Toolbox, which will give you the fundamental matrix. It may give you better results than your code, because it is using RANSAC under the hood, which makes it robust to spurious matches. There is also a triangulate function, as of version R2014b.
What you are getting is sparse 3D reconstruction. You can plot the resulting 3D points, and you can map the color of the corresponding pixel to each one. However, for what you want, you would have to fit a surface or a triangular mesh to the points. Unfortunately, I can't help you there.
If what you're asking is how to I proceed from fundamental Matrix + corresponding points to a dense model then you still have a lot of work ahead of you.
relative camera locations (R,T) can be calculated from a fundamental matrix assuming you know the internal camera params (up to scale, rotation, translation). To get a full dense matrix there are a few ways to go. you can try using an existing library (PMVS for example). I'd look into OpenMVG but I'm not sure about matlab interface.
Another way to go, you can compute a dense optical flow (many available for matlab). Look for a epipolar OF (It takes a fundamental matrix and restricts the solution to lie on the epipolar lines). Then you can triangulate every pixel to get a depthmap.
Finally you will have to play with format conversions to get from a depthmap to VRML (You can look at meshlab)
Sorry my answer isn't more Matlab oriented.

Sparse 3D reconstruction MATLAB example

I have a stereo camera system and I am trying this MATLAB's Computer Vision toolbox example (http://www.mathworks.com/help/vision/ug/sparse-3-d-reconstruction-from-multiple-views.html) with my own images and camera calibration files. I used Caltech's camera calibration toolbox (http://www.vision.caltech.edu/bouguetj/calib_doc/).
First I tried each camera separately based on first example and found the intrinsic camera calibration matrices for each camera and saved them. I also undistorted the left and right images using Caltech toolbox. Therefore I commented out the code for that from MATLAB example.
Here are the instrinsic camera matrices:
K1=[1050 0 630;0 1048 460;0 0 1];
K2=[1048 0 662;0 1047 468;0 0 1];
BTW, these are the right and center lenses from bumblebee XB3 cameras.
Question: aren't they supposed to be the same?
Then I did stereo calibration based on fifth example. I saved the rotation matrix (R) and translation matrix (T) from that. Therefore I commented out the code for that from MATLAB example.
Here are the rotation and translation matrices:
R=[0.9999 -0.0080 -0.0086;0.0080 1 0.0048;0.0086 -0.0049 1];
T=[120.14 0.55 1.04];
Then I fed all these images and calibration files and camera matrices to the MATLAB example and tried to find the 3-D point cloud but the results are not promising. I am attaching the code here. I think here are two problems:
1- My epipolar constraint value is too large!(to the power of 16)
2- I am not sure about the camera matrices and how I calculated them from R, and T from Caltech toolbox!
P.S. as far as feature extraction goes that is fine.
would be great if someone can help.
clear
close all
clc
files = {'Left1.tif';'Right1.tif'};
for i = 1:numel(files)
files{i}=fullfile('...\sparse_matlab', files{i});
images(i).image = imread(files{i});
end
figure;
montage(files); title('Pair of Original Images')
% Intrinsic camera parameters
load('Calib_Results_Left.mat')
K1 = KK;
load('Calib_Results_Right.mat')
K2 = KK;
%Extrinsics using stereo calibration
load('Calib_Results_stereo.mat')
Rotation=R;
Translation=T';
images(1).CameraMatrix=[Rotation; Translation] * K1;
images(2).CameraMatrix=[Rotation; Translation] * K2;
% Detect feature points and extract SURF descriptors in images
for i = 1:numel(images)
%detect SURF feature points
images(i).points = detectSURFFeatures(rgb2gray(images(i).image),...
'MetricThreshold',600);
%extract SURF descriptors
[images(i).featureVectors,images(i).points] = ...
extractFeatures(rgb2gray(images(i).image),images(i).points);
end
% Visualize several extracted SURF features from the Left image
figure; imshow(images(1).image);
title('1500 Strongest Feature Points from Globe01');
hold on;
plot(images(1).points.selectStrongest(1500));
indexPairs = ...
matchFeatures(images(1).featureVectors, images(2).featureVectors,...
'Prenormalized', true,'MaxRatio',0.4) ;
matchedPoints1 = images(1).points(indexPairs(:, 1));
matchedPoints2 = images(2).points(indexPairs(:, 2));
figure;
% Visualize correspondences
showMatchedFeatures(images(1).image,images(2).image,matchedPoints1,matchedPoints2,'montage' );
title('Original Matched Features from Globe01 and Globe02');
% Set a value near zero, It will be used to eliminate matches that
% correspond to points that do not lie on an epipolar line.
epipolarThreshold = .05;
for k = 1:length(matchedPoints1)
% Compute the fundamental matrix using the example helper function
% Evaluate the epipolar constraint
epipolarConstraint =[matchedPoints1.Location(k,:),1]...
*helperCameraMatricesToFMatrix(images(1).CameraMatrix,images(2).CameraMatrix)...
*[matchedPoints2.Location(k,:),1]';
%%%% here my epipolarConstraint results are bad %%%%%%%%%%%%%
% Only consider feature matches where the absolute value of the
% constraint expression is less than the threshold.
valid(k) = abs(epipolarConstraint) < epipolarThreshold;
end
validpts1 = images(1).points(indexPairs(valid, 1));
validpts2 = images(2).points(indexPairs(valid, 2));
figure;
showMatchedFeatures(images(1).image,images(2).image,validpts1,validpts2,'montage');
title('Matched Features After Applying Epipolar Constraint');
% convert image to double format for plotting
doubleimage = im2double(images(1).image);
points3D = ones(length(validpts1),4); % store homogeneous world coordinates
color = ones(length(validpts1),3); % store color information
% For all point correspondences
for i = 1:length(validpts1)
% For all image locations from a list of correspondences build an A
pointInImage1 = validpts1(i).Location;
pointInImage2 = validpts2(i).Location;
P1 = images(1).CameraMatrix'; % Transpose to match the convention in
P2 = images(2).CameraMatrix'; % in [1]
A = [
pointInImage1(1)*P1(3,:) - P1(1,:);...
pointInImage1(2)*P1(3,:) - P1(2,:);...
pointInImage2(1)*P2(3,:) - P2(1,:);...
pointInImage2(2)*P2(3,:) - P2(2,:)];
% Compute the 3-D location using the smallest singular value from the
% singular value decomposition of the matrix A
[~,~,V]=svd(A);
X = V(:,end);
X = X/X(end);
% Store location
points3D(i,:) = X';
% Store pixel color for visualization
y = round(pointInImage1(1));
x = round(pointInImage1(2));
color(i,:) = squeeze(doubleimage(x,y,:))';
end
% add green point representing the origin
points3D(end+1,:) = [0,0,0,1];
color(end+1,:) = [0,1,0];
% show images
figure('units','normalized','outerposition',[0 0 .5 .5])
subplot(1,2,1); montage(files,'Size',[1,2]); title('Original Images')
% plot point-cloud
hAxes = subplot(1,2,2); hold on; grid on;
scatter3(points3D(:,1),points3D(:,2),points3D(:,3),50,color,'fill')
xlabel('x-axis (mm)');ylabel('y-axis (mm)');zlabel('z-axis (mm)')
view(20,24);axis equal;axis vis3d
set(hAxes,'XAxisLocation','top','YAxisLocation','left',...
'ZDir','reverse','Ydir','reverse');
grid on
title('Reconstructed Point Cloud');
First of all, the Computer Vision System Toolbox now includes a Camera Calibrator App for calibrating a single camera, and also support for programmatic stereo camera calibration. It would be easier for you to use those tools, because the example you are using and the Caltech Calibration Toolbox use somewhat different conventions.
The example uses the pre-multiply convention, i.e. row vector * matrix, while the Caltech toolbox uses the post-multiply convention (matrix * column vector). That means that if you do use the camera parameters from Caltech, you would have to transpose the intrinsic matrix and the rotation matrices. That could be the main cause of your problems.
As far as the intrinsics being different between your two cameras, that is perfectly normal. All cameras are slightly different.
It would also help to see the matched features that you've used for triangulation. Given that you are reconstructing an elongated object, it doesn't seem too surprising to see the reconstructed points form a line in 3D...
You could also try rectifying the images and doing a dense reconstruction, as in the example I've linked to above.

Rotating Axes Around Line of Fit MATLAB

I'm currently frustrated by the following problem:
I've got trajectory data (i.e.: Longitude and Latitude data) which I interpolate to find a linear fitting (using polyfit and polyval in matlab).
What I'd like to do is to rotate the axes in a way that the x-axis (the Longitude one) ends up lying on the best-fit line, and therefore my data should now lie on this (rotated) axis.
What I've tried is to evaluate the rotation matrix from the slope of the line-of-fit (m in the formula for a first grade polynomial y=mx+q) as
[cos(m) -sin(m);sin(m) cos(m)]
and then multiply my original data by this matrix...to no avail!
I keep obtaining a plot where my data lay in the middle and not on the x-axis where I expect them to be.
What am I missing?
Thank you for any help!
Best Regards,
Wintermute
A couple of things:
If you have a linear function y=mx+b, the angle of that line is atan(m), not m. These are approximately the same for small m', but very different for largem`.
The linear component of a 2+ order polyfit is different than the linear component of a 1st order polyfit. You'll need to fit the data twice, once at your working level, and once with a first order fit.
Given a slope m, there are better ways of computing the rotation matrix than using trig functions (e.g. cos(atan(m))). I always try to avoid trig functions when performing geometry and replace them with linear algebra operations. This is usually faster, and leads to fewer problems with singularities. See code below.
This method is going to lead to problems for some trajectories. For example, consider a north/south trajectory. But that is a longer discussion.
Using the method described, plus the notes above, here is some sample code which implements this:
%Setup some sample data
long = linspace(1.12020, 1.2023, 1000);
lat = sin ( (long-min(long)) / (max(long)-min(long))*2*pi )*0.0001 + linspace(.2, .31, 1000);
%Perform polynomial fit
p = polyfit(long, lat, 4);
%Perform linear fit to identify rotation
pLinear = polyfit(long, lat, 1);
m = pLinear(1); %Assign a common variable for slope
angle = atan(m);
%Setup and apply rotation
% Compute rotation metrix using trig functions
rotationMatrix = [cos(angle) sin(angle); -sin(angle) cos(angle)];
% Compute same rotation metrix without trig
a = sqrt(m^2/(1+m^2)); %a, b are the solution to the system:
b = sqrt(1/(1+m^2)); % {a^2+b^2 = 1}, {m=a/b}
% %That is, the point (b,a) is on the unit
% circle, on a line with slope m
rotationMatrix = [b a; -a b]; %This matrix rotates the point (b,a) to (1,0)
% Generally you rotate data after removing the mean value
longLatRotated = rotationMatrix * [long(:)-mean(long) lat(:)-mean(lat)]';
%Plot to confirm
figure(2937623)
clf
subplot(211)
hold on
plot(long, lat, '.')
plot(long, polyval(p, long), 'k-')
axis tight
title('Initial data')
xlabel('Longitude')
ylabel('Latitude')
subplot(212)
hold on;
plot(longLatRotated(1,:), longLatRotated(2,:),'.b-');
axis tight
title('Rotated data')
xlabel('Rotated x axis')
ylabel('Rotated y axis')
The angle you are looking for in the rotation matrix is the angle of the line makes to the horizontal. This can be found as the arc-tangent of the slope since:
tan(\theta) = Opposite/Adjacent = Rise/Run = slope
so t = atan(m) and noting that you want to rotate the line back to horizontal, define the rotation matrix as:
R = [cos(-t) sin(-t)
sin(-t) cos(-t)]
Now you can rotate your points with R

How to plot a second graph instead of color coding in matlab

i just started with my master thesis and i already am in trouble with my capability/understanding of matlab.
The thing is, i have a trajectory on a surface of a planet/moon whatever (a .mat with the time, and the coordinates. Then i have some .mat with time and the measurement at that time.
I am able to plot this as a color coded trajectory (using the measurement and the coordinates) in scatter(). This works awesomely nice.
However my problem is that i need something more sophisticated.
I now need to take the trajectory and instead of color-coding it, i am supposed to add the graph (value) of the measurement (which is given for each point) to the trajectory (which is not always a straight line). I will added a little sketch to explain what i want. The red arrow shows what i want to add to my plot and the green shows what i have.
You can always transform your data yourself: (using the same notation as #Shai)
x = 0:0.1:10;
y = x;
m = 10*sin(x);
So what you need is the vector normal to the curve at each datapoint:
dx = diff(x); % backward finite differences for 2:end points
dx = [dx(1) dx]; % forward finite difference for 1th point
dy = diff(y);
dy = [dy(1) dy];
curve_tang = [dx ; dy];
% rotate tangential vectors 90° counterclockwise
curve_norm = [-dy; dx];
% normalize the vectors:
nrm_cn = sqrt(sum(abs(curve_norm).^2,1));
curve_norm = curve_norm ./ repmat(sqrt(sum(abs(curve_norm).^2,1)),2,1);
Multiply that vector with the measurement (m), offset it with the datapoint coordinates and you're done:
mx = x + curve_norm(1,:).*m;
my = y + curve_norm(2,:).*m;
plot it with:
figure; hold on
axis equal;
scatter(x,y,[],m);
plot(mx,my)
which is imo exactly what you want. This example has just a straight line as coordinates, but this code can handle any curve just fine:
x=0:0.1:10;y=x.^2;m=sin(x);
t=0:pi/50:2*pi;x=5*cos(t);y=5*sin(t);m=sin(5*t);
If I understand your question correctly, what you need is to rotate your actual data around an origin point at a certain angle. This is pretty simple, as you only need to multiply the coordinates by a rotation matrix. You can then use hold on and plot to overlay your plot with the rotated points, as suggested in the comments.
Example
First, let's generate some data that resembles yours and create a scatter plot:
% # Generate some data
t = -20:0.1:20;
idx = (t ~= 0);
y = ones(size(t));
y(idx) = abs(sin(t(idx)) ./ t(idx)) .^ 0.25;
% # Create a scatter plot
x = 1:numel(y);
figure
scatter(x, x, 10, y, 'filled')
Now let's rotate the points (specified by the values of x and y) around (0, 0) at a 45° angle:
P = [x(:) * sqrt(2), y(:) * 100] * [1, 1; -1, 1] / sqrt(2);
and then plot them on top of the scatter plot:
hold on
axis square
plot(P(:, 1), P(:, 2))
Note the additional things have been done here for visualization purposes:
The final x-coordinates have been stretched (by sqrt(2)) to the appropriate length.
The final y-coordinates have been magnified (by 100) so that the rotated plot stands out.
The axes have been squared to avoid distortion.
This is what you should get:
It seems like you are interested in 3D plotting.
If I understand your question correctly, you have a 2D curve represented as [x(t), y(t)].
Additionally, you have some value m(t) for each point.
Thus we are looking at the plot of a 3D curve [x(t) y(t) m(t)].
you can easily achieve this using
plot3( x, y, m ); % assuming x,y, and m are sorted w.r.t t
alternatively, you can use the 3D version of scatter
scatter3( x, y, m );
pick your choice.
Nice plot BTW.
Good luck with your thesis.