论文标题
语义OCTREE模型的信息理论抽象用于集成感知和计划
Information-theoretic Abstraction of Semantic Octree Models for Integrated Perception and Planning
论文作者
论文摘要
在本文中，我们开发了一种方法，该方法使自主机器人能够从点云数据构建和压缩语义环境表示。我们的方法从传感器数据中构建了环境的三维语义树表示，然后通过一种新型的信息理论树木修复方法压缩。所提出的方法是概率的，并将其纳入现实环境中固有的语义分类中。此外，我们的方法允许机器人在生成压缩树时优先考虑单个语义类，以设计保留相关语义信息的多分辨率表示，同时丢弃不需要的语义类别。我们通过压缩大型户外，语义丰富，真实世界环境的语义OCTREE模型来演示这种方法。此外，我们还展示了如何使用OCTREE抽象来创建语义信息图以进行运动计划，并使用未知的图形构造方法（例如Halton序列）对我们的方法进行比较。
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.
