How does the "Fit LiDAR to Control Points" algorithm work?

When processing LiDAR data in the past with other software, I have had the option of choosing how the LiDAR data is rectified based on the trajectory of the platform (IMU) collecting the data. This adjustment of the trajectory is then applied to the LiDAR point cloud. I know GM does not use the trajectory, but does  "Fit LiDAR to Control Points" function follow one of the following:
1. non-rigid, with translation - all the lidar points or shifted globally,  then the LiDAR points around the control points are shifted locally and all LiDAR points in between are interpolated. 
2. non-rigid, no translation - there is no shift globally, the LiDAR points around the control points are shifted locally and there is NO interpolation.
3. Rigid, with translation - all the lidar points or shifted globally, there is are no local adjustments made around the control points.

Global Mapper 18.2 + LiDAR module
Windows 10
Intel Core i7-5960X @ 3.00Ghz
32.0 GB
DerrickW
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Answers

  • csnokecsnoke Posts: 11
    Why is there no answer to this question? Seems critical.
  • csnokecsnoke Posts: 11
    So I've done some testing here, using the following process:

    I created a 10km wide rectangle at 0m elevation, then created a Elevation Grid. I then exported this surface as a LAZ using 10m post spacing to create a perfectly flat point cloud at zero elevation and containg 1.1 million points. I then created (using CAD) 22 control points distributed as a rectangular grid across this surface, these are synthetic control points:


    I then began a process of altered the elevation of a single control point, then performed a "Fit to Control" function, and seeing what effect it had on the rest of the control points. 

    In the case that you have just one control point, the entire point cloud will raise/lower by the vertical difference between the cloud and the control. In this case it is a simple, rigid, vertical translation.

    However, if you have two control points with a great vertical disparity, something more interesting occurs. In this case, I change the vertical of point 18 to +100m and 22 to -100m and constrained only those two points. The control fit results are below, it is nearly an even gradient, though the edges do flare out, indicating some interpolation. Note, it does NOT average the control point differences and apply a single vertical shift:



    My last check was to apply a "taco shell" to the cloud, putting points 11 &14 at -100, while 7 & 9 were at +100. I wanted to see what would happen at points 19 & 23. I was most concerned that it would warp the data even outside of the control, creating a "tilted plane" where the data outside of the control was shifted WAY too much. It does not do that however, instead giving some weighting to the points original elevation:



    Interesting results. I'm not sure what the technical name is for what it is doing - seems to depend on the scenario, but it seems reasonable to me. I've shared the testing data, it can be downloaded here if you want to do your own tests:

    https://www.dropbox.com/s/2u3chpsm0rv12am/Global_mapper_control_point_testing.zip?dl=0

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