情報理論による意味オクトリー抽象化：知覚と計画の統合
Information-theoretic Abstraction of Semantic Octree Models for Integrated Perception and Planning
点群データから意味的な3次元オクトリー表現を構築し、情報理論的な枝刈りで圧縮する手法を提案。圧縮結果を計画用グラフ生成に活用し、屋外環境で有効性を示した。
著者: Daniel T. Larsson, Arash Asgharivaskasi, Jaein Lim, Nikolay Atanasov, Panagiotis Tsiotras
分類: cs.RO, cs.IT, math.IT
原文アブストラクト
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.
