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I'm not really familiar with rtabmap, but if the message is a geometry_msgs/Pose, then it won't work with the state estimation nodes in r_l. Timestamps and covariances are critical to state estimation, so it's impossible for the filter to fuse measurements from those sources without having that data. The only option would be to just assume a timestamp and make up arbitrary covariances, which would not be a great idea.

However, modifying the package to output the right message type (a PoseWithCovarianceStamped) ought to be pretty straightforward. Just make sure the time stamp you use is the time stamp of the sensor data that was used to generate the pose, and if you have to make up covariance values, I'd err on the side of over-inflation. If you have the resources to do proper analysis and generate realistic covariances, then that's obviously better, but that probably won't be a trivial task.