  In this paper, we develop an approach that enables autonomous robots to build
and compress semantic environment representations from point-cloud data. Our
approach builds a three-dimensional, semantic tree representation of the
environment from sensor data which is then compressed by a novel
information-theoretic tree-pruning approach. The proposed approach is
probabilistic and incorporates the uncertainty in semantic classification
inherent in real-world environments. Moreover, our approach allows robots to
prioritize individual semantic classes when generating the compressed trees, so
as to design multi-resolution representations that retain the relevant semantic
information while simultaneously discarding unwanted semantic categories. We
demonstrate the approach by compressing semantic octree models of a large
outdoor, semantically rich, real-world environment. In addition, we show how
the octree abstractions can be used to create semantically-informed graphs for
motion planning, and provide a comparison of our approach with uninformed graph
construction methods such as Halton sequences.
