Intelligent Navigation and Localization System for Indoor Dynamic Environments via Semantic Dimension Chain Knowledge Base Model

Abstract

This paper presents an innovative intelligent navigation and localization system designed for indoor dynamic environments, leveraging the Semantic Dimension Chain Knowledge Base Model (SDC-KBM). By modeling semantic maps, the system generates the Semantic Dimension Chain (SDC) and introduces the Semantic Dimension Chain Localization (SDCL) algorithm, enabling robust real-time localization in dynamic settings. The SDC is further modeled to build SDC-KBM, which consists of regional, instance, and operational layers. Based on this model, we propose the Semantic-Geometric Pattern-based Path Planning (SGPP) algorithm, which overcomes the low intelligence of traditional path planning methods and significantly enhances real-time performance. Additionally, a task rule-based semantic parsing algorithm interprets human instructions through SDC-KBM, allowing robots to adaptively navigate based on user intent and environmental semantics. Experimental results from real-world scenarios demonstrate that SDCL outperforms state-of-the-art localization algorithms, particularly in challenging corridor environments. Meanwhile, SGPP reduces processing time by 58.90% compared to Dijkstra and 38.49% compared to A-star.