I am trying to mesh a point cloud, and calculate Gaussian and Mean curvature. I am using Meshlab.
I am trying to measure the average point cloud density, to be used in measuring curvature (APSS). The function "Radius from density" of a point cloud is confusing, and also does not include a read out.
Is there a way I can do this in Meshlab. Apologies - I am new to all of this.
Here is the receipt:
compute radius as an estimation of the local density. To do this just type 'radius density' in the search box and choose the first filter. This filter computes for each vertex a value that is the average distance of the 'k' nearest neighbours (default k=16)
map the radius into the quality (type 'per vertex quality function' in the search box and choose the filter with that name. In the parameter func just type 'rad' and check the 'map into color' option. This filter will transfer the just computed radius per vertex value into the quality and will map this value into a standard rainbow color map.
type in the search box 'show quality hist' and choose (again :)) that option.
At this point you should get something similar to this...
Related
I've successfully calibrated my camera and I can get the dimensions of a XLD in world coordinates with ContourToWorldPlaneXld and then HeightWidthRatioXld. This returns me the measures of a contour extracted from a shape.
Now I need to convert a value inserted by the user in mm (example in mm: 0.1) and get how many pixels the measure is, for example, to draw a line.
I need the pixel value as per request. I tried looking around in the Halcon documentation but I didn't find what I was looking for.
Also I read this answer but it' not exactly what I'm looking for.
I'm using Halcon Progress 21.11.
Edit: A possible solution could be obtaining the dimensions before converting them to world plane and then do something like pixel/world but I would prefer a better method if it exists.
I would like to calculate the Horizontal and Vertical field of view from the camera intrinsic matrix for the cameras used in the KITTI dataset. The reason I need the Field of view is to convert a depth map into 3D point clouds.
Though this question has been asked quite a long time ago, I felt it needed an answer as I ran into the same issue and was unable to find any info on it.
I have however solved it using the information available in this document and some more general camera calibration documents
Firstly, we need to convert the supplied disparity into distance. This can be done through fist converting the disp map into floats through the method in the dev_kit where they state:
disp(u,v) = ((float)I(u,v))/256.0;
This disparity can then be converted into a distance through the default stereo vision equation:
Depth = Baseline * focal length/ Disparity
Now come some tricky parts. I searched high and low for the focal length and was unable to find it in documentation.
I realised just now when writing that the baseline is documented in the aforementioned source however from section IV.B we can see that it can be found in P(i)rect indirectly.
The P_rects can be found in the calibration files and will be used for both calculating the baseline and the translation from uv in the image to xyz in the real world.
The steps are as follows:
For pixel in depthmap:
xyz_normalised = P_rect \ [u,v,1]
where u and v are the x and y coordinates of the pixel respectively
which will give you a xyz_normalised of shape [x,y,z,0] with z = 1
You can then multiply it with the depth that is given at that pixel to result in a xyz coordinate.
For completeness, as P_rect is the depth map here, you need to use P_3 from the cam_cam calibration txt files to get the baseline (as it contains the baseline between the colour cameras) and the P_2 belongs to the left camera which is used as a reference for occ_0 files.
I'm trying to use a VectorNav VN100 IMU to map a path through an underground tunnel (GPS denied environment) and am wondering what is the best approach to take to do this.
I get lots of data points from the VN100 these include: orientation/pose (Euler angles, quaternions), and acceleration and gyroscope values in three dimensions. The acceleration and gyro values are given in raw and filtered formats where filtered outputs have been filtered using an onboard Kalman filter.
In addition to IMU measurements I also measure GPS-RTK coordinates in three dimensions at the start and end-points of the tunnel.
How should I approach this mapping problem? I'm quite new to this area and do not know how to extract position from the acceleration and orientation data. I know acceleration can be integrated once to give velocity and that in turn can be integrated again to get position but how do I combine this data together with orientation data (quaternions) to get the path?
In robotics, Mapping means representing the environment using perception sensor (like 2D,3D laser or Cameras).
Once you got the map, it can be used by robot to know its location(Localization). Map is also used for find a path between locations to move from one place to another place(Path planning).
In your case you need a perception sensor to get the better location estimation. With only IMU you can track the position using Extended Kalman filter(EKF) but it drifts quickly.
Robot Operating System has EKF implementation you can refer it.
Ok so I came across a solution that gets me somewhat closer to my goal of finding the path travelled underground, although it is by no means the final solution I'm posting my algorithm here in the hopes that it helps someone else.
My method is as follows:
Rotate the Acceleration vector A = [Ax, Ay, Az] output by the VectorNav VN100 into the North, East, Down frame by multiplying by the quaternion VectorNav output Q = [q0, q1, q2, q3]. How to multiply a vector by a quaternion is outlined in this other post.
Basically you take the acceleration vector and add a fourth component on to the end of it to act as the scalar term, then multiply by the quaternion and it's conjugate (N.B. the scalar terms in both matrices should be in the same position, in this case the scalar quaternion term is the first term, so therefore a zero scalar term should be added on to the start of the acceleration vector) e.g. A = [0,Ax,Ay,Az]. Then perform the following multiplication:
A_ned = Q A Q*
where Q* is the complex conjugate of the quaternion (i, j, and k terms are negated).
Integrate the rotated acceleration vector to get the velocity vector: V_ned
Integrate the Velocity vector to get the position in north, east, down: R_ned
There is substantial drift in the velocity and position due to sensor bias which causes drift. This can be corrected for somewhat if we know the start and end velocity and start and end positions. In this case the start and end velocities were zero so I used this to correct the drift in the velocity vector.
Uncorrected Velocity
Corrected Velocity
My final comparison between IMU position vs GPS is shown here (read: there's still a long way to go).
GPS-RTK data vs VectorNav IMU data
Now I just need to come up with a sensor fusion algorithm to try to improve the position estimation...
I am currently creating a feature and patterning it across a flat plane to get the maximum number of features to fit on the plane. I do this frequently enough to warrant building some sort of marcro for this if possible. The issue that I run into is I still have to manually set the spacing between the parts. I want to be able to create a feature and have it determine "best" fit spacing given an area while avoiding overlaps. I have had very little luck finding any resources describing this. Any information or links to potentially helpful resources on this would be much appreciated!
Thank you.
Before, you start the linear pattern bit:
Select the face2 of that feature2, get the outer most loop2 of edges. You can test for that using loop2.IsOuter.
Now:
if the loop has one edge: that means it's a circle and the spacing must superior to the circle's radius
if the loop has more that one edge, that you need to calculate all the distances between the vertices and assume that the largest distance is the safest spacing.
NOTA: If one of the edges is a spline, then you need a different strategy:
You would need to convert the face into a sketch and finds the coordinates of that spline to calculate the highest distances.
Example: The distance between the edges is lower than the distance between summit of the splines. If the linear pattern has the a vertical direction, then spacing has to be superior to the distance between the summit.
When I say distance, I mean the distance projected on the linear pattern direction.
I need use the data of this site: http://www.navcen.uscg.gov/?Do=gpsArchives&path=2012
to develop a small software that plot a chart about satellate availability, something like this: http://i.stack.imgur.com/X0iGL.jpg
The user must set a day, a latitude/longitude position and a time zone, then my application must plot the satellate availability for 7 days (from user day) to choose the best day.
I'm not a GPS expert so I don't know which and how use the data from almanac to make the plot.
Any idea?
If you're good at Matlab you could try using this
This program calculate GPS visible satellites by exerting terrain for high accuracy prediction.
inputs:
Coordinate of Station on earth.
GPS Almanac file.
Terrain Data in "txt" format(just for DSM calculation).
Or you could go through the gpstk code and refer to how ComputeStationSatelliteVisibility l is implemented
The almanac contains the elipse parameters that describe the curve of one satellite around the earth. Using these params you could determine where the sats are positioned for a specific time and position.
Assume a visibility of 170° of the sky: 5° are hidden by houses or mountains at the horizon.
Refer to :
http://www.navcen.uscg.gov/?pageName=gpsAlmanacs
and
http://www.navcen.uscg.gov/pdf/gps/Programmatically%20Accessing.pdf