Robust Localization Method for Mobile Robot Using Feature Map in Structured Environment

Purpose Mobile robot localization in structured environments, such as warehouses, is frequently hindered by ghosting inherent in maps generated via 2D Laser Range Finder (LRF)-based SLAM. To address this issue, this study aims to introduce an enhanced localization framework specifically engineered to mitigate these ghosting effects. By effectively filtering out map noise, the proposed method significantly improves positioning robustness and precision, overcoming the limitations often encountered by traditional systems in such scenarios. Design/methodology/approach The proposed method introduces a novel system to construct optimized feature maps from raw SLAM output, effectively filtering ghosting noise while significantly reducing data volume. Subsequently, the Iterative Closest Point (ICP) algorithm is used to register real-time laser scans against this feature map for precise pose estimation. The framework’s efficacy is validated through comprehensive comparative evaluations in both simulated environments and real-world physical scenarios against conventional baselines. Findings Experimental results demonstrate that the proposed method significantly outperforms conventional approaches. In real-world tests, the root-mean-square error (RMSE) for translation and rotation was reduced by 0.034 m and 0.6, respectively. Furthermore, the system showed improved efficiency, with computational time decreased by nearly 8.2%. These findings confirm that the feature map-based approach effectively mitigates ghosting, enhancing both localization accuracy and operational efficiency. Originality/value This study addresses the persistent issue of map degradation in 2D SLAM without relying on complex multi-sensor fusion. By innovatively optimizing map representation through a specialized feature map generation mechanism, the method simultaneously compresses data and eliminates perceptual aliasing. This offers a robust, cost-effective solution for industrial automation in complex, structured environments.