I want to write a program which shows a visual animation of the orbit of satellite in 3D space, with the Earth's rotation.
I can write a code which shows a visualisation of the orbit (simply comet3()). It is possible to rotate the 3D model of the Earth either.
But I can't merge these two programs.
I've seen some Youtube videos like "Satellite Orbit Analysis and Simulation (in MATLAB)". How has he done it?
Is there any special stackexchange site for Matlab questions?
You can see a demo how to draw the Earth in 3D or 2D here:
Earth's Topography
To rotate an object like surface you can use function ROTATE. For example:
rotate(hsurf, [0 0 1], 20) #% rotates surface with handle hsurf around z axis by 20 deg
In addition have a look at Orbit Determination Toolbox (ODTBX).
And yeh, the best MATLAB SE site is here at SO. Just add or search for matlab tag.
UPDATE: Another beautiful Earth plot at FileExchange: http://www.mathworks.com/matlabcentral/fileexchange/25048
Consider doing the graphical front-end in Java. MATLAB interfaces flawlessly with Java, and it's much easier to do GUI stuff in Java. If you don't know Java and you have time, start learning, it's worth the effort as a general purpose programming language that's everywhere, and it's an invaluable companion to MATLAB.
Related
I'm modifying a matlab code. It displays graphics using the Psychtoolbox, which can basically create an on-screen window. The code I want to adapt uses both higher-level Psychtoolbox commands and lower-level OpenGL calls, as provided by the Matlab OpenGL toolbox. I'm familiar with Psychtoolbox, not at all familiar with OpenGL.
Coordinates in Psychtoolbox are in pixels and start on (0,0) from the top-left corner of the screen and move rightwards (x) and downwards (y).
I simply need the conversion between the Matlab implementation of OpenGL coordinates and Psychtoolbox's pixel-based ones. There are a few questions and answers and many resources online about this, but I am still confused.
For example, as far as I understand OpenGL uses normalized coordinates that range between [-1, 1]. However, in the code I am adapting something is nicely displayed despite y = -1.5.
So my questions are:
How do I convert between Matlab's OpenGL and Matlab's Psychtoobox coordinates?
Can OpenGL coordinates in Matlab go beyond the [-1, 1] range?
I am trying to detect corners (x/y coordinates) in 2D scatter vectors of data.
The data is from a laser rangefinder and our current platform uses Matlab (though standalone programs/libs are an option, but the Nav/Control code is on Matlab so it must have an interface).
Corner detection is part of a SLAM algorithm and the corners will serve as the landmarks.
I am also looking to achieve something close to 100Hz in terms of speed if possible (I know its Matlab, but my data set is pretty small.)
Sample Data:
[Blue is the raw data, red is what I need to detect. (This view is effectively top down.)]
[Actual vector data from above shots]
Thus far I've tried many different approaches, some more successful than others.
I've never formally studied machine vision of any kind.
My first approach was a homebrew least squares line fitter, that would split lines in half resurivly until they met some r^2 value and then try to merge ones with similar slope/intercepts. It would then calculate the intersections of these lines. It wasn't very good, but did work around 70% of the time with decent accuracy, though it had some bad issues with missing certain features completely.
My current approach uses the clusterdata function to segment my data based on mahalanobis distance, and then does basically the same thing (least squares line fitting / merging). It works ok, but I'm assuming there are better methods.
[Source Code to Current Method] [cnrs, dat, ~, ~] = CornerDetect(data, 4, 1) using the above data will produce the locations I am getting.
I do not need to write this from scratch, it just seemed like most of the higher-class methods are meant for 2D images or 3D point clouds, not 2D scatter data. I've read a lot about Hough transforms and all sorts of data clustering methods (k-Means etc). I also tried a few canned line detectors without much success. I tried to play around with Line Segment Detector but it needs a greyscale image as an input and I figured it would be prohibitivly slow to convert my vector into a full 2D image to feed it into something like LSD.
Any help is greatly appreciated!
I'd approach it as a problem of finding extrema of curvature that are stable at multiple scales - and the split-and-merge method you have tried with lines hints at that.
You could use harris corner detector for detecting corners.
I'm very new to 3D image processing.i'm working in my project to find the perspective angle of an circle.
A plate having set of white circles,using those circles i want to find the rotation angles (3D) of that plate.
For that i had finished camera calibration part and got camera error parameters.The next step i have captured an image and apply the sobel edge detection.
After that i have a little bit confusion about the ellipse fitting algorithm.i saw a lot of algorithms in ellipse fit.which one is the best method and fast method?
after finished ellipse fit i don't know how can i proceed further?how to calculate rotation and translation matrix using that ellipse?
can you tell me which algorithm is more suitable and easy. i need some matlab code to understand concept.
Thanks in advance
sorry for my English.
First, find the ellipse/circle centres (e.g. as Eddy_Em in other comments described).
You can then refer to Zhang's classic paper
https://research.microsoft.com/en-us/um/people/zhang/calib/
which allows you to estimate camera pose from a single image if some camera parameters are known, e.g. centre of projection. Note that the method fails for frontal recordings, i.e. the more of a perspective effect, the more accurate your estimate will be. The algorithm is fairly simple, you'll need a SVD and some cross products.
I have a list of 200 points I garnered from a graph digitization software I would like to transform into a smooth curve and then into Solidworks.
My points form an ellipse (airfoil shape to be more precise), so the commands I've tried in Matlab didn't have a circular curve.
My issues are:
* Obtaining a smooth curve that doesn't necessarily pass through all points, smooth being motus operandi.
* Being able to have a elliptical curve
* Somehow being able to export this curve into Solidwords
If anyone knows the right software, command line or anything that could get me started, I would be extremely thankful.
imacube
I've used Solid Works before. It's a very powerful tool. There should be some way to draw a curved spline through these points, such as a cubic spline.
If you are using a standard(ish) airfoil, then you can use a variety of tools to plot the points without having to use a graph digitization software.
Javafoil, for instance, is one of those. Even if you know the characteristics of your airfoil, you can use this to give you a smooth set of points.
Again, if your airfoil is a naca 4-series, then these are governed by a set of equations.
But I take it that the airfoil you want a more complicated one. Let me know if I can help anymore.
Anyone have any starting tips for me? I want to learn from this (ie Don't want to be lazy and have someone answer this for me).
I would like to develop my understanding of mathematical 3D surfaces. My own personal project is to produce a 3D surface/graph of the concourse structure in MATLAB.
I found a link with good pictures of its geometry here. I am not expecting to get it 100% perfectly but I'd like to come close!
At the end of this exercise I would like to have a mathematical definition of the geometry as well as a visual representation of the surface. This can involve cartesian equations, parametric equations, matrices, etc.
Any help would be very much appreciated!
To give some specific advice for MATLAB:
I would load in the 'section' image from the web page you have linked, and display this in a MATLAB figure window. You can then try plotting lines over the top until you find one that fits nicely. So you might do something like:
A = imread('~/Desktop/1314019872-1244-n364-1000x707.jpg');
imshow(A)
hold on
axis on
%# my guess at the function - obviously not a good fit
x = [550:900];
plot(x, 0.0001*x.^2 + 300)
Of course, you might want to move the position of the origin or crop the picture and so on.
As an arguably better alternative to this trial-and-error method, you could trace the outline of the section (e.g by clicking points with something like ginput), and then use one of MATLAB's curve-fitting tools (e.g. fit) to fit a function to the data.
The final 3D shape looks to me (at a casual glance) to be a 3D revolution of the section shape around a central axis. Use of a cylindrical coordinate system could therefore be a good idea.
The final plotting of your 3D shape could be done with a function such as surf or mesh.
I would start by defining a function that defines for each x, y coordinate whether there is a point z, and if so with which altitude.
The shape reminds me a bit of a log or a square root.