Semi-open and chaotic environments of building sites are considered primary challenges for the localization of mobile construction robots. To mitigate environmental limitations, an improved trilateral localization technique based on artificial landmarks fusing the extended Kalman filters (EKFs) is proposed in this paper. The reflective intensity of the onboard laser is employed to identify artificial landmarks arranged in the ongoing construction environment. A trilateral positioning algorithm is then adopted and evaluated based on artificial landmarks. Multi-sensor fusion, combined with the EKF, is included to improve the positioning accuracy and reliability of the robot in complex conditions. We constructed validation scenarios in the Gazebo simulation environment to verify the required localization functionality. Concurrently, we established simulated testing environments in real-world settings, where the practicality of the proposed technique was validated through the fitting of ideal and actual localization trajectories. The effectiveness of the proposed technique was corroborated through comparative experimental results.

Scan matching methods have been widely applied in the fields of autonomous localization and mapping. However, in structured environments where feature differences are less significant, such as long straight corridors, conventional positioning algorithms often suffer from characteristic mismatching, resulting in lower accuracy. As such, a new global localization algorithm, based on environmental difference evaluation and correlation scan matching fusion, is proposed in this study. In this process, the surrounding space was evaluated using a priori understanding of the environment based on a linear fit. Corresponding evaluation and positioning results from correlation scan matching were then modified using dynamic selection and a posture updating strategy. The performance of the proposed technique was compared with other conventional methods using open datasets exhibiting long straight features and a series of tests conducted in a physical corridor. Results showed that the proposed algorithm could effectively improve localization accuracy in narrow environments. The translation and rotation absolute pose errors were reduced by an average of 27.29% and 25.82%, respectively, compared with a correlation matching approach that does not consider the surrounding geometry. These results suggest the proposed technique offers higher adaptability and positioning accuracy in narrow environments.

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.