  This paper addresses a multi-robot mission planning problem in uncertain
semantic environments. The environment is modeled by static labeled landmarks
with uncertain positions and classes giving rise to an uncertain semantic map
generated by semantic SLAM algorithms. Our goal is to design control policies
for sensing robots so that they can accomplish complex collaborative high level
tasks captured by global temporal logic specifications. To account for
environmental and sensing uncertainty, we extend Linear Temporal Logic (LTL) by
including sensor-based predicates allowing us to incorporate uncertainty and
probabilistic satisfaction requirements directly into the task specification.
The sensor-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 can be updated online to adapt to the map that is continuously
learned by existing semantic SLAM methods. We provide extensive experiments
that corroborate the theoretical analysis and show that the proposed algorithm
can address large-scale planning tasks in the presence of uncertainty.
