GeoReferencing – Georeference XYZ Point Cloud

georeferencinglidarpoint cloud

I am looking for help. I have done a little bit of geo-registration of images with QGIS. But now, I have been handed some LIDAR data in PCD format (basically x,y,z), But it is not geo-referenced.

I have looked at: Georeferencing LAS files in geographic coordinates (lat/lon) or in cartesian (projected) ones?, but I do not have LAS data, and that article talks nothing about what would work for a GUI to do GeoReferencing, as QGIS provides.

I know the exact area and have obtained a GeoTiff from Google Earth. The entire region is only about 30 acres. So I am assuming that a simple linear transform of the values will be sufficient. But I am was wondering if any GUI tools could help with the conversion (at least getting the ground control points to line up and the linear formula). I have used QGIS to do this for unknown raster data, but this is the first time I have been asked to do this for point cloud.

My simple research has found a ideas, which likely won't work, but maybe someone can tell me what does work:

I have found an interesting free tool called cloud compare which if I understand the demos, I might be able to use (it looks like it needs two point clouds, but I could convert the GeoTiff with FME to a point cloud). I do not have ESRI available. Is Cloud Compare able to do this? How hard would it be?

If we want to put out $400 perhaps the Blue Marble folks can do this with their LIDAR plug in: http://forum.globalmapperforum.com/discussion/7444/georeference-static-lidar-data , does anyone recommend that?

I have also see this in the Matlab Vision Toolkit: http://www.mathworks.com/help/vision/ref/pcregrigid.html#bux42c5

Best Answer

PDAL can do this for you, but not automatically. You need to use something to compute your transformation matrix and then apply the transformation matrix with PDAL's filters.transformation filter. Once you have your transformation matrix, you can use the transformation in a pipeline as shown in the example.

OSGeo4W64 is a binary distribution for Windows that contains this software, or you can build it yourself using Homebrew on OSX. There are RPMs for Linux too at this location, but building on Linux is straight forward. See the compilation doc for more information.

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[GIS] DEM creation with C++ application

If you're comfortable building software from source and are on a Unix flavor or OSX, you can go from .las to DEM using PDAL and points2grid. If you're on Windows, I have no idea if you'll be able to get points2grid to build, but you could try.

Both PDAL and points2grid have C++ APIs that you could integrate into your own C++ application. You can also use PDAL's command line executable to create DEMs.

The short instructions for a Unix/OSX system to get a points2grid-enabled PDAL command-line executable are:

Make sure you have cmake; use your favorite package manager to install
Download points2grid from Github: git clone https://github.com/CRREL/points2grid.git
Build and install points2grid:

cd points2grid && cmake . && make && make install && cd ..

If you don't have write access to /usr/local, change that last bit to sudo make install
Download PDAL from Github: git clone https://github.com/PDAL/PDAL.git
Build and install PDAL with points2grid support:

cd PDAL && cmake . -DBUILD_PLUGIN_P2G=ON && make && make install && cd ..

Same caveat to make install as above

Write a pipeline xml file to describe your gridding process (run points2grid --help for more information about the points2grid options):

<?xml version="1.0" encoding="utf-8"?>
<Pipeline version="1.0">
 <Writer type="writers.p2g">
  <Option name="grid_dist_x">6.0</Option>
  <Option name="grid_dist_y">6.0</Option>
  <Option name="filename">outfile</Option>
  <Option name="output_type">mean</Option>
  <Option name="output_type">idw</Option>
  <Option name="output_format">grid</Option>
  <Reader type="readers.las">
   <Option name="filename">mylasfile.las</Option>
</Reader>

Use pdal pipeline to create your DEM:

pdal pipeline mypipeline.xml

That should create some outfile.xxx raster files.

[GIS] Why ground model from point cloud in Global Mapper is inaccurate

Is my point cloud inaccurate because it's not LiDAR?

Not exactly. All types of point clouds (from laser scanning, stereo images, etc) can have varying degree of accuracy, resolution, type of information, etc.

You can try the following to generate a better output:

Check for outliers.

First thing, check if the point cloud has any type of outliers. If it has, it can compromise all subsequent processing steps in the analysis. See Editing LiDAR point cloud to remove noise/outliers present below and above ground?.
Set the ground filtering algorithm/tool's parameters accordingly.

It is clear from the DTM (which is a TIN representing the terrain surface) image that it was generated using non-ground points to triangulate as well (see the bigger triangles which are exactly where the trees are). Global Mapper's Lidar Automatic Ground Classification has some parameters which are user-defined. Understanding what each one does and testing/setting them accordingly is essential to avoid labeling errors (classifying non-ground points as ground).

For example, given the scene is significantly flat, and the point cloud is of low to medium resolution:
- Set the Maximum Height Delta parameter to a value smaller than the height of the tallest trees (e.g. 70% of average height from trees). This should eliminate the presence of major trees in the DTM.
- Set the Base Bin Size parameter based on the point cloud's point spacing (at least 2 times the average point spacing) AND make sure it will also be greater than the largest tree crown diameter in the scene. This will make sure every bin has at least one actual ground point to compare with. This is also valid for the same parameter when generating the DTM/DEM (see here).
- Start with the default value for Minimum Height Departure from Local Mean, but also test greater values.
This will help minimizing labeling errors, but will generalize the DTM as well. You can make tests until you find the best trade off.
Another method for ground point classification.

Take a look in additional options for ground point cloud classification. For example, Determining bare earth DEM from unclassified LAS file?. Likewise in step 1, in every tool/algorithm, make sure all parameters were set accordingly, so be sure the best of each tool/algorithm was tested.
Generalize the DTM by making a DEM (raster surface).

A Digital Elevation Model (DEM) is a gridded raster representation of the DTM (see What is the difference between DEM, DSM and DTM?), i.e., a form of generalizing the DTM.

Depending on your needs about the terrain information (some uses of it need more accuracy than others), you can generate a generalized DEM with a large pixel size (the same reasoning explained for ground classification in #2) so to capture more than one ground point per pixel and avoid interpolation.

In Global Mapper, assuming the point cloud is already correctly classified, Create Elevation Grid from 3D Vector Data (with LiDAR data), select the Grid Method as Binning (Minimum Value - DTM) or Binning (Average Value).

_{Considering the final goal to visualize terrain information and considering previous results obtained with the TIN-DTM, do not select Binning (Maximum Value - DSM) as the presence of non-ground points (outliers) might shift the DEM upwards; and also do not select Triangulation (Grid TIN of Points) as it will produce a raster surface with pixel size smaller than most of the triangles in the intermediate TIN and will yield similar results to the TIN-DTM you already have, see here}.

Don't forget to filter by class code 2 (ground) in Filter LiDAR points ... as well.

Last, you can also try smoothing/denoising the output DEM, in the most possible way. See some GIS SE posts about DEM generalization/smoothing:

Here is a YouTube tutorial video from Blue Marble Webinars with more info about making DTM and DEM in Global Mapper.

Best Answer

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