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This repository has been archived by the owner on Sep 2, 2024. It is now read-only.
I am trying to use this algorithm to replace my PCL ICP based system but have not been able to get any decent transformations. I am trying to align a laser scanned model of an object with the scene point cloud of the object in a basic environment. I followed the instructions to create input for the algorithm and got outputs as follows:
This is clearly not correct as there is a rotation to align the clouds.
I tried playing with some of the parameters but have not gotten better results.
Do you have any tips for getting better results? I attached two sample files, one of the model and one of the scene, to see if anyone is able to get a good transformation that aligns the model (sample_files.zip)
Thanks!
The text was updated successfully, but these errors were encountered:
Fast global matching utilizes local 3D feature. I guess the provided bottle_full.pcd is laser scanline. This seems to be too thin to extract distinctive local feature that can be matched for bottle_model.pcd. This is why no matching is survived in [tuple constaint] step. We recommend to use more denser point cloud such as depth map of this bottle template.
The point cloud attached is segmented out of the point cloud returned from a Microsoft Kinect and has the front face of the bottle. Do you think that is not enough for a match? Do you think it would be a better idea to try matching the entire unsegmented scene point cloud?
Hi,
I am trying to use this algorithm to replace my PCL ICP based system but have not been able to get any decent transformations. I am trying to align a laser scanned model of an object with the scene point cloud of the object in a basic environment. I followed the instructions to create input for the algorithm and got outputs as follows:
ReadFeature ... done.
ReadFeature ... done.
normalize points :: mean[0] = [-0.202804 -0.075849 0.622425]
normalize points :: mean[1] = [0.015940 -0.047936 0.001295]
normalize points :: global scale : 1.000000
Advanced matching : [0 - 1]
points are remained : 2451
[cross check] points are remained : 11
[tuple constraint] 0 tuples (1100 trial, 1100 actual).
[final] matches 0.
Pairwise rigid pose optimization
with transformations of
0 1 2
1.0000000000 0.0000000000 0.0000000000 -0.2187434137
0.0000000000 1.0000000000 0.0000000000 -0.0279131606
0.0000000000 0.0000000000 1.0000000000 0.6211291552
0.0000000000 0.0000000000 0.0000000000 1.0000000000
This is clearly not correct as there is a rotation to align the clouds.
I tried playing with some of the parameters but have not gotten better results.
Do you have any tips for getting better results? I attached two sample files, one of the model and one of the scene, to see if anyone is able to get a good transformation that aligns the model (sample_files.zip)
Thanks!
The text was updated successfully, but these errors were encountered: