In this article, we propose the novel concept of Belief Scene Graphs, which are utility-driven extensions of partial 3D scene graphs, that enable efficient high-level task planning with partial information. We propose a graph-based learning methodology for the computation of belief (also referred to as expectation) on any given 3D scene graph, which is then used to strategically add new nodes (referred to as blind nodes) that are relevant for a robotic mission. We propose the method of Computation of Expectation based on Correlation Information (CECI), to reasonably approximate real Belief/Expectation, by learning histograms from available training data. A novel Graph Convolutional Neural Network (GCN) model is developed, to learn CECI from a repository of 3D scene graphs. As no database of 3D scene graphs exists for the training of the novel CECI model, we present a novel methodology for generating a 3D scene graph dataset based on semantically annotated real-life 3D spaces. The generated dataset is then utilized to train the proposed CECI model and for extensive validation of the proposed method. We establish the novel concept of \textit{Belief Scene Graphs} (BSG), as a core component to integrate expectations into abstract representations. This new concept is an evolution of the classical 3D scene graph concept and aims to enable high-level reasoning for the task planning and optimization of a variety of robotics missions. The efficacy of the overall framework has been evaluated in an object search scenario, and has also been tested on a real-life experiment to emulate human common sense of unseen-objects.

翻译：本文提出信念场景图（Belief Scene Graphs）这一新概念，它是部分三维场景图的效用驱动扩展，能够在信息不完全的情况下实现高效高层任务规划。我们提出一种基于图的学习方法，用于计算任意给定三维场景图上的信念（亦称为期望），该信念随后被策略性地用于添加与机器人任务相关的新节点（称为盲节点）。我们提出基于相关性信息的期望计算方法（CECI），通过从可用训练数据中学习直方图，合理逼近真实信念/期望。开发了一种新型图卷积神经网络（GCN）模型，用于从三维场景图库中学习CECI。由于不存在用于训练新型CECI模型的三维场景图数据库，我们提出了一种基于语义标注的真实三维空间生成三维场景图数据集的新方法。利用生成的数据集训练所提出的CECI模型，并对所提方法进行广泛验证。我们建立了信念场景图（BSG）这一新概念，作为将期望融入抽象表示的核心组件。这一新概念是经典三维场景图概念的演进，旨在实现各类机器人任务规划与优化的高层推理。整个框架的有效性已在物体搜索场景中进行了评估，并在真实实验中测试了其模拟人类对未见物体常识的能力。