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Run ct_icp on Jetson Nano #53
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Hello, So we use the default Ceres solver (which should be DENSE_QR). What is costly in our method is the construction of the neighborhoods (and the point associations), |
Hello @pdell-kitware thank you for your answer. 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? |
What do you suggest to speed up the lidar odometry? |
Hello, I would like to run this slam on my Jetson Nano 4Gb.
I have manage to install and build on it, but even if I use Ceres as solver, I can not manage to run the solver on the board GPU.
I know that only some type of Ceres option are currently supperted by CUDA:
"CUDA If you have an NVIDIA GPU then Ceres Solver can use it accelerate the solution of the Gauss-Newton linear systems using the CMake flag USE_CUDA. Currently this support is limited to using the dense linear solvers that ship with CUDA. As a result GPU acceleration can be used to speed up DENSE_QR, DENSE_NORMAL_CHOLESKY and DENSE_SCHUR. This also enables CUDA mixed precision solves for DENSE_NORMAL_CHOLESKY and DENSE_SCHUR. Optional."
So, I would like to know which dense linear solver have you use.
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