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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.
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.