用先验知识提升机器人对传感器数据的物体理解能力
ExPrIS: Knowledge-Level Expectations as Priors for Object Interpretation from Sensor Data
- 构建3D语义场景图，融合过往观察与外部知识图谱
- 通过异构图神经网络实现带先验偏置的推理过程
- 适合需要长期稳定场景理解的移动机器人应用
尽管深度学习显著推动了机器人物体识别的发展，但纯数据驱动方法常缺乏语义一致性，难以利用环境中已有的先验知识。本报告介绍ExPrIS项目，探索如何利用知识级期望来改进从传感器数据中对物体的解释。方法基于逐步构建3D语义场景图（3DSSG），融合来自过去观测的上下文先验和来自ConceptNet等外部知识图谱的语义知识，嵌入异构图神经网络（GNN）中，形成带有先验偏置的推理机制。该方法超越静态逐帧分析，增强了场景理解在时间维度上的鲁棒性与一致性。报告详细阐述了该架构、评估结果，并规划在移动机器人平台上的集成方案。
原文摘要 · Abstract (English)
While deep learning has significantly advanced robotic object recognition, purely data-driven approaches often lack semantic consistency and fail to leverage valuable, pre-existing knowledge about the environment. This report presents the ExPrIS project, which addresses this challenge by investigating how knowledge-level expectations can serve as to improve object interpretation from sensor data. Our approach is based on the incremental construction of a 3D Semantic Scene Graph (3DSSG). We integrate expectations from two sources: contextual priors from past observations and semantic knowledge from external graphs like ConceptNet. These are embedded into a heterogeneous Graph Neural Network (GNN) to create an expectation-biased inference process. This method moves beyond static, frame-by-frame analysis to enhance the robustness and consistency of scene understanding over time. The report details this architecture, its evaluation, and outlines its planned integration on a mobile robotic platform.
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