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

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.