  The fusion of independently obtained stochastic maps by collaborating mobile
agents is considered. The proposed approach includes two parts: matching of
stochastic maps and maximum likelihood alignment. In particular, an affine
invariant hypergraph is constructed for each stochastic map, and a bipartite
matching via a linear program is used to establish landmark correspondence
between stochastic maps. A maximum likelihood alignment procedure is proposed
to determine rotation and translation between common landmarks in order to
construct a global map within a common frame of reference. A main feature of
the proposed approach is its scalability with respect to the number of
landmarks: the matching step has polynomial complexity and the maximum
likelihood alignment is obtained in closed form. Experimental validation of the
proposed fusion approach is performed using the Victoria Park benchmark
dataset.
