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It seems that you are running 2 SLAM algorithms in parallel which is a bit confusing, but you could still try one of the following :

1) The output of SLAM is {map+global pose estimate} so you actually have 2 different maps with 2 different global pose estimates. There is no trivial way to fuse all these directly AFAIK. I guess if you are not interested in fusing the maps, you could take one of them as a reference and then just fuse the global pose estimates through an EKF/UKF via the robot_localization package.

2) If one of these 2 SLAM algorithms was using odometry as an input and you already had a source of odometry (e.g wheel odometry, visual odometry...) you could try to use the other SLAM algorithm to provide an alternative odometry estimate, and then fuse it through an EKF/UKF with your existing odometry. Then give the "improved" (?) odometry estimate to your second SLAM algorithm.

For instance, hector_slam can provide such an odometry estimate via the undocumented pub_odometry parameter, see this question

The problem here is that none of the two SLAM algorithms you mentioned - I am less familiar with PTAM though - use any source of odometry... You could try to modify hector_slam to use odometry but that requires some work, see this question. As for PTAM, I don't know.