Robust Real-Time Localization System via Semantic Dimensional Chains for Degraded Scenarios

作     者：Li, Yunfei Jiang, Lin Lei, Bin Tang, Bo Zhu, Jianyang 

作者机构：Wuhan Text Univ Sch Mech Engn & Automat Wuhan 430081 Peoples R China 

出 版 物：《IEEE INTERNET OF THINGS JOURNAL》 (IEEE Internet Things J.)

年 卷 期：2025年第12卷第9期

页      面：12579-12588页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Key Research and Development Program of China [2019YFB1310000] National Natural Science Foundation of China Key Research and Development Program of Hubei Province of China [2020BAB098] 

主　　题：Semantics Location awareness Robots Lighting Visualization Real-time systems YOLO Robot vision systems Cameras Accuracy Degraded scenarios indoor robot localization semantic graph matching sensors fusion 

摘      要：To overcome the limitations of current visual and laser SLAM methods in narrow, feature-repetitive, and lighting-variable degraded scenarios, we propose a robust real-time mapping and localization system utilizing semantic dimensional chains (SDCs). SDC is a novel ordered semantic instance map representation. During the mapping phase, our system efficiently integrates diverse object detection and 2D-SLAM algorithms, constructing a prior semantic map through Bayesian filtering. In the preprocessing phase, a semantic map optimization algorithm based on neighborhood homogeneity is applied to effectively eliminate semantic noise. For localization, SDCs combined with precise localization algorithms enable rapid global localization. The system also features a kidnapping detection mechanism for swift pose recovery. We rigorously evaluated the system s performance in terms of robustness to lighting interference, global localization, and pose recovery in real-world degraded environments. Experimental results indicate that our localization system outperforms existing methods. Compared to a state-of-the-art vision-based localization algorithm, our semantic matching (SM) module exhibits superior resistance to lighting interference, resulting in a 15.38% increase in localization success rate (SR) and a 27.71% reduction in average localization time.