benchmarking between SLAM alghoritms

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The best way to have ground truth is to make the ground the robot rolls on (literally) :) Make a Gazebo world for your robot to roll around in so that you know exactly where everything is to compare to. Then run the different SLAM implementations over the _exact same_ mapping run's data of that space and compare.

As for comparison techniques, many are available, I'd recommend consulting other SLAM benchmarking papers for their methodologies and using one or a variant of one for formal comparison.

As @stevemacenski said, easiest way to test their accuracy is a simulation environment which you could directly access to the ground truth data. This was what I did to compare effects of navigation's parameters' changes 3 years ago.

But; maybe in a testing environment, you could measure exact positions of multiple points in a room, then navigate the robot manually to those positions continuously on maps generated by both those algorithms. After navigating between those points enough, you could have enough data to compare the performance.

Or let's say that this is an academic project and you could create an environment which you can place different sized AR tags everywhere. You can continuously collect the differences between navigation beliefs on generated maps by both packages and AR tag positioning reports.

I don't really know to answer your first question, but I would suggest to try running several SLAM algorithms and keep as ground truth the one that you think performs better.

As for the second question, there exists this package https://github.com/robotics-4-all/ogm_merging that implements the evaluation between two different OGMs.
It is applying the Nearest Neighbor method, for every point of the map that is considered to be an obstacle, calculates the distance between these points and finds the Overall Mean Square Error.