Iterative closest point for 3D alignment in LabVIEW using PCL (point cloud library)

[Klemen]

Hello,

Today I started working on porting the ICP algorithm from the PCL library (http://pointclouds.org/). This is such an excellent library for dealing with the point clouds and I have been using it to previously to acqure the data stream from the Kinect device.

I have created a dll which takes a reference 3D point cloud and another point cloud (same point cloud, but with some rigid transformation) which needs to be aligned to the first. I quickly tested the algorithm  and here are the results. A filter is also applied to the 3D point cloud to reduce the noisy surface (the surface was acquired using the Kinect). I also reduced the data so each point cloud uses only ~4000 points. The convergence time for 15 iterations is ~100 ms (10 Hz refresh rate).

Here are the results:

The gray 3D surface represents the input point cloud, which needs to be aligned with the reference point cloud (green 3D surface).

The red 3D surface represents the gray 3D surface after the ICP algorithm.

And the deviation between the reference and transformed 3D surface (the values are in meters):

Be creative.

Best regards,

K

Edited on 30.9.2013:

I replaced the images of the ICP algorithm, after optimizing some of the parameters for my case. I have also used Moving least squares filter to reduce the noise and smooth the reconstructed surface. I used the transformation matrix, which consists of the rotation matrix and the translation vector. Applying this transforms the input pointcloud to the target pointcloud based on the ICP algorithm.

See:

https://decibel.ni.com/content/blogs/kl3m3n/2014/02/16/3d-point-cloud-registration-in-labview-using-...

for source/Labview code and example.