PCL Developers blog

Aravindhan K Krishnan

This is my personal page

project:Automated Point Cloud registration

About me

I am a Ph.D student at School of Earth and Space Exploration, Arizona State University. I am interested in Robot perception, and currently working on registration of large point clouds.


Please find it here.

Recent status updates

Frustum Culling
Sunday, September 30, 2012

Filters points lying within the frustum of the camera. The frustum is defined by pose and field of view of the camera. The parameters to this method are horizontal FOV, vertical FOV, near plane distance and far plane distance. I have added this method in the filters module. The frustum and the filtered points are shown in the images below.

../../_images/frustum-culling-1.png ../../_images/frustum-culling-2.png ../../_images/frustum-culling-3.png
ShadowPoints filter
Sunday, July 08, 2012

This filter removes the ghost points that appear on the edges. This is done by thresholding the dot product of the normal at a point with the point itself. Points that obey the thresholding criteria are retained. This completes the port of libpointmatcher to PCL. Now, I will be writing examples for all the techniques I added.

SamplingSurfaceNormal filter
Wednesday, July 04, 2012

This filter recursively divides the data into grids until each grid contains a maximum of N points. Normals are computed on each grid. Points within each grid are sampled randomly and the computed normal is assigned to these points. This is a port from libpointmatcher.

Adding a new filtering technique
Saturday, May 12, 2012

I am working on a new filtering technique that divides the point cloud into boxes that have similar densities and approximates the box by the centroid. This will be completed soon. Parallely I am also working on the registration tutorial that I mentioned in the previous blog post.

Correspondence rejection based on computing an optimal inlier ratio
Sunday, April 22, 2012

This method extends the existing outlier rejection method ‘CorrespondenceRejectionTrimmed’ based on the fraction of inliers. The optimal inlier ratio is computed internally instead of using a user specified value. The method is described in the paper ‘Outlier Robust ICP for Minimizaing Fractional RMSD’ by Jeff M. Philips et al. I am adding the test code in the test folder. This wraps up the porting of correspondence rejection methods from libpointmather to PCL. Next I am planning to add some filtering methods. Before that I will be adding tutorials on using the registration pipleline in PCL.