Information-Theoretic Abstraction of Semantic Octree Models for Integrated Perception and Planning

In this paper, we develop an approach that enables autonomous robots to build and compress semantic environ- ment representations from point-cloud data. Our approach builds a three-dimensional, semantic tree representation of the environment from raw sensor data which is then compressed by a novel information-theoretic tree-pruning approach. The proposed approach is probabilistic and incorporates the un- certainty in semantic classification inherent in real-world envi- ronments. 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 pro- vide a comparison of our approach with uninformed graph construction methods such as Halton sequences.