  Mapping in the GPS-denied environment is an important and challenging task in
the field of robotics. In the large environment, mapping can be significantly
accelerated by multiple robots exploring different parts of the environment.
Accordingly, a key problem is how to integrate these local maps built by
different robots into a single global map. In this paper, we propose an
approach for simultaneous merging of multiple grid maps by the robust motion
averaging. The main idea of this approach is to recover all global motions for
map merging from a set of relative motions. Therefore, it firstly adopts the
pair-wise map merging method to estimate relative motions for grid map pairs.
To obtain as many reliable relative motions as possible, a graph-based sampling
scheme is utilized to efficiently remove unreliable relative motions obtained
from the pair-wise map merging. Subsequently, the accurate global motions can
be recovered from the set of reliable relative motions by the motion averaging.
Experimental results carried on real robot data sets demonstrate that proposed
approach can achieve simultaneous merging of multiple grid maps with good
performances.
