  This paper addresses a multi-robot planning problem in environments with
partially unknown semantics. The environment is assumed to have known geometric
structure (e.g., walls) and to be occupied by static labeled landmarks with
uncertain positions and classes. This modeling approach gives rise to an
uncertain semantic map generated by semantic SLAM algorithms. Our goal is to
design control policies for robots equipped with noisy perception systems so
that they can accomplish collaborative tasks captured by global temporal logic
specifications. To specify missions that account for environmental and
perceptual uncertainty, we employ a fragment of Linear Temporal Logic (LTL),
called co-safe LTL, defined over perception-based atomic predicates modeling
probabilistic satisfaction requirements. The perception-based LTL planning
problem gives rise to an optimal control problem, solved by a novel
sampling-based algorithm, that generates open-loop control policies that are
updated online to adapt to a continuously learned semantic map. We provide
extensive experiments to demonstrate the efficiency of the proposed planning
architecture.
