kl3m3n's blog
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lizhuo_lin
on:
Stereo vision (OpenCV and Labview comparison)
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张斌
on:
Point cloud registration tool
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GohanTYO
on:
Qt+PCL+OpenCV (Kinect 3D face tracking)
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Klemen
on:
Qt GUI for PCL (OpenNI) Kinect stream
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efreet
on:
OpenCV and Qt based GUI (Hough circle detection example)
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Spalabuser
on:
Homography mapping calculation (Labview code)
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rameshr
on:
Optical water level measurements with automatic water refill in Labview
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xuexue0224
on:
Kalman filter (OpenCV) and MeanShift (Labview) tracking
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Aaatif
on:
Serial data send with CRC (cyclic redundancy check) - Labview and Arduino/ARM
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hsaid
on:
Color image segmentation based on K-means clustering using LabVIEW Machine Learning Toolkit
3D point cloud registration in Labview using PCL - Point cloud library (C++ source code, dll, Labview example)
Hello,
turns out I have a little bit more time, so the next post is here! 
I am just going to paste the intro to this post from the end of the previous post titled "Kinect in Labview using PCL - Point cloud library (C++ source code, dll, Labview example)":
"My next post will be talking about point cloud registration (also in Labview using PCL). I have used ICP (iterative closest point) algorithm in one of the previous posts to align two point clouds, but no coarse alignment/registration was made prior to the refined ICP registration. So in some cases, the alignment using only ICP is incorrect (mostly for greater degree of transformation - rotation and translation - between two successively acquired point clouds)."
So for example, if you want to align the following point clouds (similar object/surface, but different position in space - so two acquired point clouds from different point of view) the ICP yields an incorrect result - in my case the algorithm does not converge:
Figure 1. Source point cloud/surface (gray) and the target point cloud/surface (green).
Using the feature based initial alignment (MORE INFORMATION ABOUT THE REGISTRATION AND ITS PARAMETERS etc... CAN BE OBTAINED FROM THE PCL web site, so I will not go into these details here) the result is:
Figure 2. Source point cloud/surface (gray), the target point ploud/surface (green) and the transformed source point cloud/surface (red) after initial alignment.
And after the initial alignment, the ICP algorithm produces the following result:
Figure 3. Source point cloud/surface (gray), the target point ploud/surface (green) and the transformed source point cloud/surface (red) after initial alignment and ICP.
Also, the point clouds are filtered prior to registration using the MLS filter, that is not included (this is also a part of the PCL library and I use a seperate .dll for filtering). The Labview example is saved for Labview 2010.
I am attaching the source code, dll and a Labview example program. You need PCL 1.6.0 to be able to run/modify the example. The .dll was built using Visual Studio 2010x86.
Best regards,
K
