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hku-mars / livox_camera_calib

This repository is used for automatic calibration between high resolution LiDAR and camera in targetless scenes.
GNU General Public License v2.0
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velodyne support #7

Open gonultasbu opened 3 years ago

gonultasbu commented 3 years ago

I have been searching for a targetless automatic lidar-camera calibration package and I came across your repository, however the info section on README says that Velodyne support is to be added later. I was wondering why is it not possible to calibrate Velodyne and RealSense using this package. Theoretically, I can just modify the corresponding topic names before calibration and it should be able to treat the pointcloud as a sensor_msgs/PointCloud2 object independently (from the hardware). Why is that not possible? I am willing to try it myself in a few days but wanted to consult you before that.

Thank you for your work!

ChongjianYUAN commented 3 years ago

Hello, I haven't tested the code on Velodyne, but I've tested it on Ouster. As the spinning LiDAR is repeated scanning and can not get a dense point cloud without motion, which will result in an insufficient and inaccurate number of edges extracted. Hence, I recommend adding the preprocess process(e.g. Fast-LIO) to make the point cloud of the spinning LiDAR denser. Here's an example: ouster_cloud After that, our algorithm can work well and get an accurate extrinsic value: ouster_projection The source code will be updated shortly.

gonultasbu commented 3 years ago

Running a mapping algorithm to get a denser LiDAR map indeed makes sense, thanks!

zzkslam commented 3 years ago

Running a mapping algorithm to get a denser LiDAR map indeed makes sense, thanks!

Can you tell me which mapping algorithm works?

gonultasbu commented 3 years ago

Running a mapping algorithm to get a denser LiDAR map indeed makes sense, thanks!

Can you tell me which mapping algorithm works?

I didn't have the time to test them right now but Fast-LIO referred by the author might come useful.

zzkslam commented 3 years ago

Running a mapping algorithm to get a denser LiDAR map indeed makes sense, thanks!

Can you tell me which mapping algorithm works?

I didn't have the time to test them right now but Fast-LIO referred by the author might come useful.

OK，thanks

CanCanZeng commented 2 years ago

Hi, I'm trying to calibrate a velodyne HDL32 lidar with a camera, as you said, the fast-lio can be used to get denser point cloud, but I find that the point cloud produced by fast-lio is defined in IMU coordinate system. So I need to transform the point from IMU to lidar first and then calibrate the transformed cloud with camera. Actually, the extrinsic between IMU and lidar is not that accurate, so how can I get an accurate extrinsic between lidar and camera? @ChongjianYUAN

islamtalha01 commented 2 years ago

Hi I have checked your git repository regarding lidar camera calibration. I am a research intern at my Home university NUST Pakistan. I have an os1 64 spinning lidar available at my lab. I need to calibrate the camera and lidar. Now kindly tell me the precise step I need to follow to get that calibration parameter. I don't know how to use Fast LIO for spinning lidar calibration. kindly outline the steps to follow to get extrinsic parameters with your package using os1 64 lidar. I would very thankful to you for this favour.I have bag file recorded with topic pointcloud2 and hikvision camera image. kindly help me in this regard

TianArthur commented 2 years ago

Hello, I haven't tested the code on Velodyne, but I've tested it on Ouster. As the spinning LiDAR is repeated scanning and can not get a dense point cloud without motion, which will result in an insufficient and inaccurate number of edges extracted. Hence, I recommend adding the preprocess process(e.g. Fast-LIO) to make the point cloud of the spinning LiDAR denser. Here's an example: ouster_cloud After that, our algorithm can work well and get an accurate extrinsic value: ouster_projection The source code will be updated shortly.

Thank you very much for your excellent work.Is there any update on work of adding fastlio for calibrating repetitive ldiar.

ChongjianYUAN commented 2 years ago

Hello, I haven't tested the code on Velodyne, but I've tested it on Ouster. As the spinning LiDAR is repeated scanning and can not get a dense point cloud without motion, which will result in an insufficient and inaccurate number of edges extracted. Hence, I recommend adding the preprocess process(e.g. Fast-LIO) to make the point cloud of the spinning LiDAR denser. Here's an example: ouster_cloud After that, our algorithm can work well and get an accurate extrinsic value: ouster_projection The source code will be updated shortly.

Thank you very much for your excellent work.Is there any update on work of adding fastlio for calibrating repetitive ldiar.

Sorry, I haven't had enough time recently to open source a version that supports Spinning LiDAR . You can try this repo: https://github.com/AFEICHINA/extended_lidar_camera_calib

kahowang commented 2 years ago

Thank you very much for your excellent work and reply~

simi-asher commented 1 year ago

Hi, if anyone is looking for an extension to spinning LiDARs but in ROS2, please check out my repository here. Happy to reply to questions or issues.