GF-SLAM: A Novel Hybrid Localization Method Incorporating Global and Arc Features

A global and feature-based hybrid algorithm, which integrates global information and feature-based simultaneous localization and mapping (GF-SLAM). This system can operate adaptively when external signals are unstable, thereby avoiding cumulative errors produced by local methods. In agricultural planting bases with abundant circular arc features, the focus is on efficiently exploring the correlations between these features to optimize the robust real-time positioning system for work vehicles. In this process, feature-based SLAM (F-SLAM) is applied to partial positioning using a particle filter. Available global information is then fused using an extended Kalman filter (EKF) for precise positioning and deviation correction in the mapping process, thereby achieving an effective combination of two positioning modes. The proposed model was evaluated using two simulation environments and a comparison with representative techniques. Results showed that GF-SLAM was competitive in normal conditions while requiring fewer computations and significantly reducing the drift in F-SLAM for stable global signals. Switching between these two algorithms eliminated positioning errors to within 1 cm for a test case in which global localization was lost in 54.7% of the route, producing an error within 3 cm. The code will be open source. Note to Practitioners—Our adaptive fusion strategy aims to address challenges in real-world agricultural scenarios where global information may be lacking or unstable. This approach enhances the robustness and reliability of robot positioning in practical applications. We invite practitioners to consider the adaptability of our system to diverse environments, particularly those with limited or fluctuating global information. In this paper, we first introduce the EKF module for global localization, then illustrate the establishment of feature maps and particle filter positioning in F-SLAM. We conduct comparison experiments in two virtual environments and real agricultural planting bases, where approximately half of the route lacks global information. The results demonstrate the benefits of our adaptive fusion strategy in practical positioning applications. We also plan to explore the integration of additional sensors, such as cameras, combined with deep learning, to further improve the efficiency and quality of feature extraction. This extension is aimed at mitigating the issue of positioning failure caused by crop and equipment occlusion in agricultural scenarios.