Run ct_icp on Jetson Nano about ct_icp HOT 3 OPEN

Hello,

So we use the default Ceres solver (which should be DENSE_QR).
To be honest, I doubt that this will speedup significantly the process, as the linear system solved if of small size (6 or 12 parameters, thus the matrix to invert is of size up to 12x12).

What is costly in our method is the construction of the neighborhoods (and the point associations),
As well as the evaluation of the residuals, rather than solving the Linear Problem.

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Hello @pdell-kitware thank you for your answer.
In fact I have notice that even on my Deskot PC the slam is a little slow to give the correct output, but in my dataset on a straight path of 250m inside a raillway tunnel it gives 20m of error that is a really good result in this type of test field (concrete flat walls, no signal, no marker,ecc...).

When you run it on a real hardware (in my case is an Ouster OS0-1) do you subsample the pointcloud to speed up?

Another thing, I have see that I can use GTSAM or CERES as solver, based on your experience which is the best in term of solving speed?

Is mandatory to use DENSE_QR as solver or I can try with DENSE_NORMAL_CHOLESKY and DENSE_SCHUR?

Last thing, I am doing these experiments because I wish to run your slam on a drone inside tunnels to make a 3D reconstruction. Have you ever test the slam performance on a real hardware with real sensors(even not on a derone)? For example I am planning to run the slam on the jetsnon and all the other thing on an i7 intel nuc. Do you think is a good choiche?

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What do you suggest to speed up the lidar odometry?

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