Alignment issue regarding temporal point cloud data

[Reji J]

Hi all,

        I am working on LiDAR data collected temporally using Faro Scanner over an agricultural field to estimate the CHM and other parameters  . The problem that I am facing now the alignment issue of the georeferenced temporal data. 

Here I am attaching the raster file to show the alignment issue. My question is : Is there a way to align the data in .las format(ie in point cloud data itself)?

[Jean-François Bourdon]

I presume that the scanner was standing on the same spot for both scans, so what I would do is to find the coordinates of this spot (see find_origin.png) and then use those as the origin of a polar coordinates system. Convert all cartesian coordinates to polar coordinates, apply the correct rotation value to one of your dataset and convert back to cartesian coordinates. There is probably other ways of aligning your two datasets, but that would be my first shot.

[Evon Silvia]

Although LAStools can perform the transformation to align both scans, it doesn't currently have cloud-to-cloud matching functionality. The open source package CloudCompare was made for this purpose, including helping to determine the differences between collections: http://cloudcompare.org/

I'm not affiliated with them in any way. Just an occasional user.

Evon

--
Download LAStools at
http://lastools.org
http://rapidlasso.com
Be social with LAStools at
http://facebook.com/LAStools
http://twitter.com/LAStools
http://linkedin.com/groups/LAStools-4408378
Manage your settings at
http://groups.google.com/group/lastools/subscribe

[Reji J]

Dear  Jean-François Bourdon,

Thank you for responding to my query. The scans' origin point is not the same for the two dates. Moreover in each dataset is around 16 scans were grouped to cover the area of interest.

I will appreciate any ideas that will work in this particular case.

Thanks and regards,

REJI J

--

[Jean-François Bourdon]

Well, because the shift is only between the datasets and not between the scans within each dataset I think that my solution would work. After correcting rotational shift, you may need to translate to get a perfect alignment of your two datasets.

You can use las2las with the parameters -rotate_xy 10.0 200000 3000000 (angle + XY origin) and -translate_xyz 5.0. 2.5 0 to alter one of your dataset.

You could also do it by scripting in python using the libLAS package or in R using the rlas package.

Other thread I just found about the same issue => Rotations and translations