For example i use Leica RTC360 scanner for terrestrial data acquisition and i like to perform a stitching with point cloud gathered from UAV photos (not lidar). What is the best way to do so?
I am pretty confident that Leica Cyclone can manage this task but i am looking for opensource (like Meshlab or CloudCompare) stable solutions for that purpose. Also in need of work flow description or experience in this case.
You might try automated data registration in cloud compare which is based on ICP (Iterative closest point) to bring the point cloud in the same coordinate system. In this case, the results depends on the initial orientations of the point cloud. If initial orientations are close to each other, then you might achieve a better result. You can do it manually by selecting a minimum of four well-distributed and recognisable points in cloud compare or you can go for more points for a least-squares solution. You may also want to try open-source code based on normal distributions transform (NDT) for registering the two data together.
Sarvesh Kumar Singh thank you for ur answer! Could u go more specific about NDT?
Hi Nikita,
NDT does not require initial alignment of point cloud and still gives better data registration although processing time is more in comparison to ICP. I would suggest using point cloud library for data stitching. It is open source and you can use the code straightaway. Here is the link to NDT and its implementation in point cloud library. If you are not sure how to use it then probably start with cloud compare and use ICP after roughly aligning your point cloud.
http://www.pointclouds.org/documentation/tutorials/normal_distributions_transform.php
https://ieeexplore.ieee.org/document/7260213
Here is MATLAB implementation of NDT
https://au.mathworks.com/help/vision/ref/pcregisterndt.html
MATLAB implementation of ICP
https://au.mathworks.com/help/vision/ref/pcregrigid.html
To achieve more accurate result you can use local point descriptors provided in point cloud library for data registration or point cloud stitching.
Regards,
Sarvesh
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