I have a question about the article "CT-ICP"

[bigbigpark]

I don't know whether this Github issue pages are right place for question...

I have read your paper CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure.

I successfully have built your ct_icp code using KITTI dataset with viz3d

Here is my question

What is exactly the role of pyLiDAR-SLAM ?

The paper says that ct_icp is lidar-only odometry and has loop closure itself using g2o

Is it enough for SLAM to use ct_icp ?

I would appreciate it if you could answer my question...!

Best regards.

[pdell-kitware]

Hi bigbigpark,

This is the right place for these questions, thanks for your interest in the project !

So in the paper, we implemented the LoopClosure in pyLidar-SLAM.

Historically, pyLiDAR-SLAM was the main project where I could implement / benchmark SLAM algorithms,
We made a wrapper for CT-ICP in python and integrated it into pyLidar-SLAM.

Since then, (and really for time management of my PhD thesis), my focus has been on C++ code and ct_icp improvements.

So we are currently working on an implementation of the loop closure in C++ (with ROS support), as we found that it was more relevant in the short-term for most use-case of SLAM. It will probably appear in another gitlab project, but there will be pointers to it in this project. It should appear in the coming weeks (there is a working version which still needs some polishing).
In the mean time, you will find the LoopClosure implementation we used in our paper in pyLidarSLAM.

However our aim is (when I will have some time) to update the python wrapping to integrate ct-icp with pyLiDAR-SLAM, so we can use the neat parameter grid-search that comes with hydra.

So right now our SLAM (in this project) is really an odometry : ie correct locally, but no compensation for large trajectory errors.

Hope it answers your question,

Best Regards,

[bigbigpark]

I totally understood what I didn't know. Thank you for your kind reply.

Thank you for sharing your wonderful project and giving good inspiration to many people.

Good luck.