用外接相机与本体论构建动态语义地图，提升机器人环境理解能力。
A hybrid pipeline for dynamic ontology-based semantic mapping
- 融合视觉定位、目标检测与本体驱动更新，实现动态语义建模。
- 通过线性回归校正坐标估计，提升空间定位精度。
- 适合需实时环境理解的机器人导航与交互任务。
语义映射对机器人与复杂环境中的物体交互、操作和导航至关重要。现有主流语义映射流程包括几何建图与定位（SLAM）、感知、语义融合及语义表示。近年研究开始引入先验知识，如知识图谱或语义场景图，以增强环境上下文理解。本文提出一种混合式语义映射流水线：系统采用外接校准相机，通过单应性投影完成几何建图与定位，结合目标检测、持久目标跟踪及本体驱动的语义更新，构建动态语义世界模型。同时使用线性回归模型校正真实世界坐标的估计值。系统持续基于实时传感数据更新物体实例、空间属性与语义关系。选择本体作为知识表示形式，因其具备层次结构、语义表达力强，并支持动态世界建模。
原文摘要 · Abstract (English)
Semantic mapping plays a crucial role in the ability of a robot to interact with objects, operate and navigate a complex environment. The most common pipeline for semantic mapping consists of geometric mapping and localization (SLAM), perception, semantic fusion and semantic representation. However, more recent works also integrate a form of prior knowledge in their application, most notably knowledge graphs or semantic scene graphs, to improve contextual understanding of the environment. In this paper, we present a hybrid pipeline for semantic mapping. Our system incorporates an external calibrated camera using homography projection for geometric mapping and localization, combined with object detection, persistent object tracking and ontology driven semantic updates to build a dynamic semantic world model. Linear regression models are also used for correction of the estimated values of real world coordinates. The system continuously updates object instances, spatial properties and semantic relations based on real time sensory data. Ontologies are selected as form of knowledge representation due to their hierarchical structure, semantic expressiveness and support for dynamic world modelling.
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