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Hi everyone, 

i am getting these kind of contours because of a misalignment in overlapping point clouds as shown in the graphic below
This is happening in various parts of a river that we were mapping last month as shown below in the blue areas
image (3).png
does anyone know how the point clouds can be aligned vertically in a uniform way so as not to lose accuracy? Or any other solution to the issue?

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Posted : October 1, 2022 2:09 am
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the registration of the point clouds should be tied to the collection platform, i.e. fixed wing/gps/imu with a base station GPS and appropriate GCPs.

If you flew a drone, similar protocols etc to tie down the elevations of the clouds, but more importantly how they're registered between each other. If the control is set up on each reach (based on what you're depicting in the image) the BMs used for each one would become the baseline for the elevation and registration of the project.

more information is needed on what differences you're seeing and what the workflow was when the collection occured.


Posted : October 1, 2022 8:28 am
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Apps like Global Mapper w/ Lidar module and TOPODOT both have a Least Squares adjustment to realign point clouds by matching a few points.

Posted : October 2, 2022 9:04 am
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Would aligning the point clouds via GCP's in Cloudcompare work?  Stated more as a question as my knowledge of the program is largely from reading and watching videos about it.  It is open source so it may be a cheap method to try first.

ie. Not exactly what you are looking for, but a very simple example of what I was talking about:

This post was modified 2 months ago by Jaccen
Posted : October 2, 2022 6:51 pm
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I can't tell how much vertical discrepancy there is between the two clouds, but if it's obviously out of tolerance I would first suspect processing methodology and/or control point values. Even different runs should be matching up in the overlap areas.

Some of our larger projects incorporate both mobile and aerial LiDAR, and the mobile mapping runs usually match up with our sUAS point clouds within 1-2cm, using different processing software, different raw data and often slightly different methodology.

Sometimes the data are gathered weeks or months apart. It's all about the control and the processing workflow.

As for fixes, cloud to cloud is going to be tricky with that small amount of overlap, especially if there aren't many identifiable features in the common areas. I wouldn't have much confidence in a river bed to extract common points in automated routines.

Posted : October 3, 2022 11:45 am