发表机构
University of Patras（帕特雷大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种混合语义映射流水线，结合SLAM、单应性投影、物体跟踪与本体更新，构建动态语义世界模型，用于机器人环境理解与交互。
AI 中文摘要
语义映射对机器人与物体交互、操作及复杂环境导航的能力至关重要。语义映射最常见的流水线由几何建图与定位（SLAM）、感知、语义融合及语义表示组成。然而，近期研究也会在应用中整合某种形式的先验知识，最显著的是知识图谱或语义场景图，以提升对环境的上下文理解。本文提出一种用于语义映射的混合流水线，系统采用经校准的外部相机，利用单应性投影进行几何建图与定位，结合物体检测、持久物体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。系统基于实时传感数据持续更新物体实例、空间属性及语义关系，选择本体作为知识表示形式，因其具备层次结构、语义表达性及对动态世界建模的支持能力。
英文摘要
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.
