  For Lifelong SLAM, one has to deal with temporary localization failures,
e.g., induced by kidnapping. We achieve this by starting a new map and merging
it with the previous map as soon as relocalization succeeds. Since
relocalization methods are fallible, it can happen that such a merge is
invalid, e.g., due to perceptual aliasing. To address this issue, we propose
methods to detect and undo invalid merges. These methods compare incoming scans
with scans that were previously merged into the current map and consider how
well they agree with each other. Evaluation of our methods takes place using a
dataset that consists of multiple flat and office environments, as well as the
public MIT Stata Center dataset. We show that methods based on a change
detection algorithm and on comparison of gridmaps perform well in both
environments and can be run in real-time with a reasonable computational cost.
