Thanks for the fix. Can you also swap the values in the hydra config so that it matches what we actually ran? (this should also fix the broken unit tests).

Ah, I missed that - thanks for the pointer! Turns out they will still fail because the noise model for the pose graph has translation and rotation in the correct order.

Should we re-run the experiment with the correct ordering, and modify the expected_losses in test_pgo_benchmark.py?

Yes, in this case I think is safe to modify expected_losses then. Thanks!

@suddhu Any plans on finalizing this PR soon? Thanks!

Yup - sorry for taking a bit! Running pytest -s tests/test_pgo_benchmark.py -s, the PGO final errors seem quite high in comparison with the previous unit test:

With the corrected rot/trans noise (latest commit on this branch):

pgo_cpu_choleskydensesolver_64_256_16.mat
[[-0.298862789376761, -0.30542158053746615, -0.27485602387525104, -0.300523110327785]]
pgo_cuda_lucudasparsesolver_64_256_16.mat
[[-0.2988627893768147, -0.3054215805374096, -0.27485602387600594, -0.3005231103295129]]
pgo_cpu_cholmodsparsesolver_64_256_16.mat
[[-0.29886278947606854, -0.3054215806396245, -0.2748560240408431, -0.30052311060619713]]

Previous result (main branch):

pgo_cpu_choleskydensesolver_64_256_16.mat
[[-0.05253952558117652, -0.06922697773243278, -0.036454724771805606, -0.06110373107271005]]
.pgo_cuda_lucudasparsesolver_64_256_16.mat
[[-0.05253952558136447, -0.06922697773270092, -0.03645472477181133, -0.06110373110087176]]
.pgo_cpu_cholmodsparsesolver_64_256_16.mat
[[-0.052539527012601166, -0.06922697775065781, -0.03645472565461781, -0.06110373151314644]]

Would that be expected, or anything I overlooked @fantaosha?

Yup - sorry for taking a bit! Running pytest -s tests/test_pgo_benchmark.py -s, the PGO final errors seem quite high in comparison with the previous unit test:

With the corrected rot/trans noise (latest commit on this branch):

pgo_cpu_choleskydensesolver_64_256_16.mat
[[-0.298862789376761, -0.30542158053746615, -0.27485602387525104, -0.300523110327785]]
pgo_cuda_lucudasparsesolver_64_256_16.mat
[[-0.2988627893768147, -0.3054215805374096, -0.27485602387600594, -0.3005231103295129]]
pgo_cpu_cholmodsparsesolver_64_256_16.mat
[[-0.29886278947606854, -0.3054215806396245, -0.2748560240408431, -0.30052311060619713]]

Previous result (main branch):

pgo_cpu_choleskydensesolver_64_256_16.mat
[[-0.05253952558117652, -0.06922697773243278, -0.036454724771805606, -0.06110373107271005]]
.pgo_cuda_lucudasparsesolver_64_256_16.mat
[[-0.05253952558136447, -0.06922697773270092, -0.03645472477181133, -0.06110373110087176]]
.pgo_cpu_cholmodsparsesolver_64_256_16.mat
[[-0.052539527012601166, -0.06922697775065781, -0.03645472565461781, -0.06110373151314644]]

Would that be expected, or anything I overlooked @fantaosha?

See my last comments. Thanks!

@suddhu This is not directly related to this PR, but could you also add the change below in pose_graph_benchmark.py. We just found out than an earlier commit broke that example. Thanks!

Yup - sorry for taking a bit! Running pytest -s tests/test_pgo_benchmark.py -s, the PGO final errors seem quite high in comparison with the previous unit test:
With the corrected rot/trans noise (latest commit on this branch):

pgo_cpu_choleskydensesolver_64_256_16.mat
[[-0.298862789376761, -0.30542158053746615, -0.27485602387525104, -0.300523110327785]]
pgo_cuda_lucudasparsesolver_64_256_16.mat
[[-0.2988627893768147, -0.3054215805374096, -0.27485602387600594, -0.3005231103295129]]
pgo_cpu_cholmodsparsesolver_64_256_16.mat
[[-0.29886278947606854, -0.3054215806396245, -0.2748560240408431, -0.30052311060619713]]

Previous result (main branch):

pgo_cpu_choleskydensesolver_64_256_16.mat
[[-0.05253952558117652, -0.06922697773243278, -0.036454724771805606, -0.06110373107271005]]
.pgo_cuda_lucudasparsesolver_64_256_16.mat
[[-0.05253952558136447, -0.06922697773270092, -0.03645472477181133, -0.06110373110087176]]
.pgo_cpu_cholmodsparsesolver_64_256_16.mat
[[-0.052539527012601166, -0.06922697775065781, -0.03645472565461781, -0.06110373151314644]]

Would that be expected, or anything I overlooked @fantaosha?

See my last comments. Thanks!

@fantaosha swapped the noise ratios and the rotation/translation in gt_relative_pose, optimization error is still high at [[-0.27113693051651366, -0.2863556061414448, -0.2404976921415048, -0.27444387002505344]].

When I swap the ordering of translation, rotation in the noise model to info = torch.tensor([1 / rotation_noise] * 3 + [1 / translation_noise] * 3, dtype=dtype).view(1, -1) the optimization does converge to [[-0.01911358713714046, -0.02313885434084465, 0.02663424963684986, 0.030410307200575255]]. But that doesn't seem consistent with the conventions, so there's probably something I'm missing!

Overridden by #560