A hybrid pipeline for dynamic ontology-based semantic mapping

2026-09-03

AI Summary

该研究提出一种结合外部校准相机、对象检测、持续跟踪及本体驱动语义更新的混合管道，以构建动态语义世界模型，解决机器人在复杂环境中的交互问题。

Abstract

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.

Problem

• semantic mapping
• dynamic ontology
• contextual understanding

Innovation

• hybrid pipeline
• dynamic ontology
• semantic mapping
• homography projection
• linear regression models