I was wondering is it the geometric kernel that works behind the curtain in CAD programs when for example, we want to draw a line from a point to midpoint of another line ? does geometric kernel keeps track of points and finds intersections, perpendecular lines and etc in CAD software or there is some other part of software that handles these things ? I hope my question is clear.
CGAL Kernel provides various geometric objects (triangles, points, segments, lines, planes,...) as well as operations on them (collinearity test, do-intersection test, intersection computation, ...).
All the object are discrete objects (but sphere and circle and maybe a few others) and there is no nurb patches for example.
I'm not sure I answered your question...
The choice depends on purposes and tasks of an application. For CAD geometry you can use Opencascade technology. Opencascade has good documentation and tutorials. Also you can see FreeCAD system (opensource) that based on Opencascade as example.
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I’d like to perform a surface parametrization of a triangle mesh (for the purpose of texture mapping).
I tried using some of CGAL’s algorithms, e.g. ARAP, Discrete Conformal Map etc.
The problem is that the surface parameterization methods proposed by CGAL only deal with meshes which are homeomorphic (topologically equivalent) to discs.
Meshes with arbitrary topology can be parameterized, provided that the user specifies a cut graph (a set of edges), which defines the border of a topological disc.
So the problem now becomes – how to cut the graph properly (using CGAL’s interface).
I found a similar question from 3 years ago that went unanswered.
P.S.
If someone can point me to a different library that can do the job, that’ll be just as great.
Thanks.
I have a very fine mesh (STL) of some organic shapes (e.g., a bone) and would like to convert it to a few patches of NURBS, which will be much smoother with reasonable simplification.
I can do this manually with Solidworks ScanTo3D function, but it is not scriptable. It's a pain when I need to do hundreds of them.
Would there be a way to automate it, e.g., with some open source libraries available? I am perfectly fine with quite some loss in accuracy. I use mainly Python, but I don't mind if it is in other languages and I can work my way around it.
Note that one thing I'd like to avoid is to convert an STL of 10,000 triangles to a NURBS with 10,000 patches. I'd like to automatically (programmatically, could be with some parameter tunings) divide the mesh into a few patches and then fit it. Again, I'm perfect fine with quite some loss in accuracy.
Converting an arbitrary mesh to nurbs is not easy in general. What is a good nurbs surface for a given mesh depends on the use case. Do you want to manually edit the nurbs surface afterwards? Should symmetric structures or other features be recognized and represented correctly in the nurbs body? Is it important to keep the volume of the body? Are there boundary lines that should not be simplified as they change the appearance or angles that must be kept?
If you just want to smooth the mesh or reduce the amount of vertices there are easier ways like mesh reduction and mesh smoothing.
If you require your output to be nurbs there are different methods leading to different topologies and approximations like indicated above. A commonly used method for object simplification is to register the mesh to some handmade prototype and then perform some smaller changes to shape the specific instance. If there are for example several classes of shapes like bones, hearts, livers etc. it might be possible to model a prototype nurbs body for each class once which defines the average appearance and topology of that organ. Each instance of a class can then be converted to a nurbs by fitting the prototype to that instance. As the topology is fixed the optimization problem is reduced to the problem where we need to find the control points that approximate the mesh with the smallest error.Disadvantage of this method is that you have to create a prototype for each class. The advantage is that the topology will be nice and easily editable.
Another approach would be to first smooth the mesh and reduce the polygon count (there are libraries available for mesh reduction) and then simply converting each triangle/ quad to a nurbs patch (like the Rhino MeshToNurb Command). This method should be easier to implement but the resulting nurbs body could have an ugly topology.
If one of this methods is applicable really depends on what you want to do with your transformed data.
I am currently working on a (school-)project involving a robot having to navigate a corn field.
We need to make the complete software in NI Labview.
Because of the tasks the robot has to be able to perform the robot has to know it's position.
As sensors we have a 6-DOF IMU, some unrealiable wheel encoders and a 2D laser scanner (SICK TIM351).
Until now I am unable to figure out any algorithms or tutorials, and thus really stuck on this problem.
I am wondering if anyone ever attempted in making SLAM work in labview, and if so are there any examples or explanations to do this?
Or is there perhaps a toolkit for LabVIEW that contains this function/algorithm?
Kind regards,
Jesse Bax
3rd year mechatronic student
As Slavo mentioned, there's the LabVIEW Robotics module that contains algorithms like A* for pathfinding. But there's not very much there that can help you solve the SLAM problem, that I am aware of. The SLAM problem consist of the following parts: Landmark extraction, data association, state estimation and updating of state.
For landmark extraction, you have to pick one or multiple features that you want the robot to recognize. This can for example be a corner or a line(wall in 3D). You can for example use clustering, split and merge or the RANSAC algorithm. I believe your laser scanner extract and store the points in a list sorted by angle, this makes the Split and Merge algorithm very feasible. Although RANSAC is the most accurate of them, but also has a higher complexity. I recommend starting with some optimal data points for testing the line extraction. You can for example put your laser scanner in a small room with straight walls and perform one scan and save it to an array or a file. Make sure the contour is a bit more complex than just four walls. And remove noise either before or after measurement.
I haven't read up on good methods for data association, but you could for example just consider a landmark new if it is a certain distance away from any existing landmarks or update an old landmark if not.
State estimation and updating of state can be achieved with the complementary filter or the Extended Kalman Filter (EKF). EKF is the de facto for nonlinear state estimation [1] and tend to work very well in practice. The theory behind EKF is quite though, but it should be a tad easier to implement. I would recommend using the MathScript module if you are going to program EKF. The point of these two filters are to estimate the position of the robot from the wheel encoders and landmarks extracted from the laser scanner.
As the SLAM problem is a big task, I would recommend program it in multiple smaller SubVI's. So that you can properly test your parts without too much added complexity.
There's also a lot of good papers on SLAM.
http://www.cs.berkeley.edu/~pabbeel/cs287-fa09/readings/Durrant-Whyte_Bailey_SLAM-tutorial-I.pdf
http://ocw.mit.edu/courses/aeronautics-and-astronautics/16-412j-cognitive-robotics-spring-2005/projects/1aslam_blas_repo.pdf
The book "Probabalistic Robotics".
https://wiki.csem.flinders.edu.au/pub/CSEMThesisProjects/ProjectSmit0949/Thesis.pdf
LabVIEW provides LabVIEW Robotics module. There are also plenty of templates for robotics module. Firstly you can check the Starter Kit 2.0 template Which will provide you simple working self driving robot project. You can base on such template and develop your own application from working model, not from scratch.
Is there any 3D CAD software out there, free-ware, shareware, open-source, or commercial, that uses the simplified instruction set made famous by the Logo Turtle (e.g. FORWARD 100, LEFT 90, etc) as the basis for a scripted 3D CAD application? The scripting approach is simple and easy because the user is always at the location of the "turtle"; the command language is easier for some people to master than the GUI interfaces of many CAD programs.
This would be geared to DIY projects, not sophisticated engineering scenarios. For example, I'd really like to make a 3D rendering of a piping schematic, and it would be really cool to use the Logo-turtle instruction-set to plot out the length and direction of the pipes. FORWARD 100, LEFT 45, FORWARD 25, UP 29, FORWARD 40, etc . Even cooler would be the ability to drop into the emerging design appropriate fittings (elbows, T, Y, couplings,adapters, etc) selected from a Toolbox, and to do things like change pipe diameter on-the-fly.
Anything like this out there?
It's hard to answer a question like this in the negative with absolute assurance, but I believe the answer is no. The reason is that rather than specifying angles to turn right and left, (i.e. one rotational degree of freedom) you'd have to have roll, pitch and yaw (three rotational degrees of freedom). This would make control of a 3d turtle a bit tricky.
Further, a user would probably like to do more than just create lines and (segmented) curves. For instance, it's hard to see how the turtle paradigm would extend to surface modeling.
There are however, free products for intuitive 3d modeling. For instance Autodesk 123D or Google Sketchup.
FMSLogo has commands for 3D drawing, for example: http://fmslogo.sourceforge.net/manual/understand-your-orientation-in-3D.html. It seems that other Logo implementations have something similar as well. But perhaps it's still far away from what #Tim was looking for.
While not Logo-based, one could script something along these lines using OpenSCAD pretty simply.
https://openscad.org/
The problem is that the 3D model gets very complex very quickly and performance bogs down.
Other similar tools include Toolpath Language: https://tplang.org/ which is integrated into the 3D previewer CAMotics: https://camotics.org/
Or, one could just use G-Code and G-Code previewer.
In case somebody doesn't know: A cartogram is a type of map where some country/region-dependent numeric property scales the respective regions so that that property's density is (close to) constant. An example is
from worldmapper.org. In this example, countries are scaled according to their population, resulting in near-constant population density.
Needless to say, this is really cool. Does anyone know of a Matplotlib-based library for drawing such maps? The method used at worldmapper.org is described in (1), so it would surprise me if no one has implemented this yet...
I'm also interested in hearing about other cartogram libraries, even if they're not made for Matplotlib.
(1) Michael T. Gastner and M. E. J. Newman,
Diffusion-based method for producing density-equalizing maps,
Proc. Nat. Acad. Sci. USA, 101, 7499-7504 (2004). Available at arXiv.
There's this, though it's based and a different algorithm (and though it's on the ESRI site, it doesn't require ArcGIS). Of course, once you have the cartogram you can plot it in matplotlib.
Here is a Javascript plugin to make cartograms using D3. It is a good, simple solution if you are not too concerned about the regions being sized accurately. If accuracy is important, there are other options available that give you more freedom to play with the algorithm's parameters to get to a more accurate result.
Here are two great standalone programs I know of:
Scapetoad
Carto3F
Scapetoad is very easy to use. Just give it a shapefile, tell it which attribute to use for the scaling, and set a few accuracy parameters. If there is any doubt, this post describes the process.
Carto3F is more complex and allows for greater accuracy, though it is a bit trickier to figure out - lots of parameter settings without much documentation explaining them.
There is also a QGIS cartogram plugin, written in Python. Though I have not been able to get it to work, so cannot comment on that one.
In short, no. But Newman has an excellent little implementation of his and Gastner's method on his website. Installing it is easy and it works from the command line. Here's an example of a workflow using this software that worked for me.
Compute a grid of density estimates over some region, e.g. in Python. Store it as a matrix of numbers.
Run the cart program with your density matrix as input from the command line or from as subprocess in Python.
The program returns a list of new coordinates for each grid point.
Pipe your shapefile points through the interp program and into a new shapefile to get the transformed map.
There are nice instructions on the main page.
The geoplot.cartogram function in
Geoplot: geospatial data visualization — geoplot 0.2.0
says it is a high-level Python geospatial plotting library, and an extension to cartopy and matplotlib.
Try this library if you are using geopandas, it is quick and doesnt require much customization. https://github.com/mthh/cartogram_geopandas