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Comment · 8 months ago
Hello Dima!

Thanks for replying - I really admire your work with mrcal, and have used it for a while in my work. And lensboy is heavily inspired by mrcal.

> Is your splined representation compatible with the mrcal splined stereographic model?

No. It was a conscious design decision to only use pinhole models, at least to begin with. As I understand it, the tradeoff is that the pinhole model does not support ultra-wide lenses like the stereoscopic model does. But I have never used a lens that has a field of view close to the limits of pinhole models, so I just went with pinhole models.

However, if I understood your documentation correctly, the distortion model should be identical, it's just the core model that is different.

> Is your splined lens representation better in some way?

I'm pretty sure it's not - I based it entirely off yours, without trying to improve it. However, I did implement regularization slightly differently.

> I didn't see any documentation about it.

Unfortunately I have not written documentation as impressive as you have in mrcal, though I aim to. I wanted to get the project working as soon as possible to use it at work.

> If you think the mrcal distribution methods could be improved, and are willing to help improve it, I would be very amenable.

I actually did attempt this in this fork: https://github.com/Robertleoj/drcal (I'm sorry for the brazen phrasing in the readme, but I believe it is sligtly in the style of the name "numpysane").

The purpose of this was just to have functionality of mrcal but available on pypi, and with a build system I understand better.

However, I abandoned this, instead opting for making a library from scratch, partly because I had a hard time removing numpysane as a dependency (I want to have very minimal dependencies in this project). I'm not sure how to reconcile this, but I'm happy to chat about what I did there.

> Do you support mrcal .cameramodel files? If not, can you do that?

I don't support them currently. I'd love to chat about doing this, even if loading them would lose some information because of different data models.

Don't hesitate to send me an email, you can find it on my github profile.

Comment · 1 year ago
Hello! I'm the creator of SlamDunk, and I use it daily in my day-to-day work for 3D visualizations.

I really like it, so I thought I'd share it.

Comment · 1 year ago
Did you try using the previous configuration as an initial guess to the numerical optimizer? If the next position is close to the previous one, the solver should be very quick.

Also, you can probably get much faster results if you obtain the gradient of the forward kinematics.

Comment · 1 year ago
Thanks! Exactly - I feel much more comfortable talking about the hardware at work now after having made this and feel more confident about making something more advanced.

This is my first printer so I don't have anything to compare it to, but to me it seems to "just work"

Comment · 1 year ago
Right? I'm thoroughly enjoying that rush :D
Comment · 1 year ago
Really cool 4dof robot - thanks for the reccomendation
Comment · 1 year ago
Thank you, great to hear!
Comment · 1 year ago
That's true - using numerical optimization was just the simplest/fastest way to get it working :P
Comment · 1 year ago
Nice - did you just chuck it into wolfram alpha or sympy? How'd you get it?

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