  With the development of cheap image sensors, the amount of available image
data have increased enormously, and the possibility of using crowdsourced
collection methods has emerged. This calls for development of ways to handle
all these data. In this paper, we present new tools that will enable efficient,
flexible and robust map merging. Assuming that separate optimisations have been
performed for the individual maps, we show how only relevant data can be stored
in a low memory footprint representation. We use these representations to
perform map merging so that the algorithm is invariant to the merging order and
independent of the choice of coordinate system. The result is a robust
algorithm that can be applied to several maps simultaneously. The result of a
merge can also be represented with the same type of low-memory footprint
format, which enables further merging and updating of the map in a hierarchical
way. Furthermore, the method can perform loop closing and also detect changes
in the scene between the capture of the different image sequences. Using both
simulated and real data - from both a hand held mobile phone and from a drone -
we verify the performance of the proposed method.
