Which hand eye calibration method is the most popular?
Hello, I spent some time looking for Implementations, that solve the Hand-Eye-Problem AX=XB. There are several repositories such as:
Is there a common and most used implementation? Which algorithm is state of the are and offers the best results? Where is its implementation?
Thanks for some hints
Nils
have you found out which is the best?
Apparently the handeye_calib_camodocal package is base on this paper leads to the most robust results.
I would love to have a python implementation of that algroithm, but have not found any implementations yet.
easy_handeye worked well for me for calibrating the rotation of a Structure Core camera mounted on the wrist of a ur5 robot. However, the translation is off by a few centimeters. Tested over multiple runs and AR markers with different shapes and sizes.
A few centimeters is a pretty big difference. Your camera calibration could be the cause of this deviation.
I was just searching information on that and I found that question quite useful! Just to add that if you want to use Ensenso, they have their own implementation in the official API.
Well, but what kind of algorithm do they use?
No idea. I am using just the API. I think the backend it's not open source, but I tought was going to work optimally in the case.