Mismatch between image and corresponding LiDAR point cloud

[nilimapaul]

I am working on deep learning-based extrinsic calibration for lidar and camera fusion and facing the following issues:

1. When I implemented the lidar-image/depth map with the same code and data given in https://github.com/unmannedlab/RELLIS-3D/tree/main/utils, I found the same result as given in this Github repo. In this result, the output seems quite reliable i.e., image and lidar data (bin file) seem to belong to the same pair. Shown below:

But the problem is when I tried with the data from a random sequence (00000,...,00004) of the Rellis-3D dataset(image, bin file, transform.yaml, camera_info.txt), downloaded from https://drive.google.com/drive/folders/1aZ1tJ3YYcWuL3oWKnrTIC5gq46zx1bMc

I found the output is not as expected. It seems the lidar data and image is not from the same pair. There is not even minimum matching with image and lidar data. The lidar points are spread randomly over the image.Though I have used image and bin file with same sequence number (ex. xxxx104.jpg and xxx104.bin) and camera_info.txt, transform.yaml from the same sequence. The output shown below:

Another example:

Therefore, please let me know what could be the possible reason for such unexpected output. I understand there must be a little miscalibration effect, but these results are absolutely undesirable.

2. Another issue I found is that the total number of images in each sequence is not the same as total number of bin files (lidar data). In every sequence, there are few more images available. So to make it consistent with the number of bin files, I deleted the last few extra images from the image folder. Now I'm not sure if any problem should occur with this.

I tried to evaluate my deep learning model for extrinsic calibration (trained on KITTI dataset) with small data from rellis-3D dataset, and I got huge errors (more than 1m translation error and 20deg+ rotation error even with the model of 5th iterative refinement which was giving pretty good result with Kitti dataset).

I wonder if the problem is with the depth image issues stated above. If there is any issue with the dataset given on the website, please provide me with the updated dataset.

Thanks in advance.

[maskjp]

Hi, @nilimapaul ,

I checked from my side, the projection is OK.
Could you tell me which data you downloaded from the google drive folder? Could you try to download from the link on github readme for each types of data?

The number of image and lidar are not consistent. you can deleted the last extra images.

About the your calibration model, reason might because there's some problem about the data or code that cause the wrong projection as you shown above. Another problem, it might because KITTI and our dataset have different setup and sensors that cause your modal can't be directly transferred to our dateset if you learn your model only on KITTI.

Hope this helps!

Best wishes!

[nilimapaul]

Hi @maskjp,

Thanks a lot for your time.
I downloaded 'Rellis_3D_vel_cloud_node_kitti_bin' file for LiDAR data (thinking of Velodyne data) and 'Rellis_3D_pylon_camera_node' file for images along with other calibration related files. Kindly let me know if I did mistake with these files.

For projection I used the same code given in your github https://github.com/unmannedlab/RELLIS-3D/blob/main/utils/lidar2img.ipynb, I tried to just run your code giving inputs to check if the code working properly at my end. The output projected depth image I have given above is output of your depth-map code only.

Apart from that I understand that the model trained on kitti dataset will not give very good result for this dataset. But I've not gone so far, before using this data in my calibration model I wanted to check the depth maps with your code only and I'm getting the above mentions issues.

Now please tell me how to get rid of this.

Thanks You!

[nilimapaul]

Hi @maskjp,
after seeing your reply today, I thought of trying with another lidar data(bin file) which is Rellis_3D_os1_cloud_node_kitti_bin, and its working fine.
Thanks.

[maskjp]

Hi, @nilimapaul,

Yes, the transformation is between the Basler camera and ouster lidar. I added the transformation between Ouster and Velodyne Lidar. You can find the example here.

I hope it helps!