Objective Underground coal mines commonly exhibit low illuminance, weak textures, and structuralization-induced feature degradation. These scenes lead to challenges of insufficient effective features and high mismatch rates to the visual simultaneous localization and mapping (SLAM) system, severely compromising its localization accuracy and robustness.
Methods This study proposed an edge awareness-enhanced visual SLAM method. First, an edge-awareness constrained low-illuminance image enhancement module was developed. Specifically, images with clear textures and uniform illumination were obtained using the Retinex algorithm optimized using an adaptive gradient-domain guided filter. This significantly improved feature extraction performance under low and uneven lighting conditions. Second, an edge awareness-enhanced feature extraction and matching module was introduced into the visual odometry. A point and line feature fusion strategy was employed to enhance the detectability and matching accuracy of weak textures and features in structured scenes. Specifically, line features were extracted using the EDLines algorithm, while point features were extracted using the Oriented FAST and Rotated BRIEF (ORB) algorithms. Such feature extraction was followed by precise feature matching achieved using grid-based motion statistics (GMS) and ratio test matching algorithms. Finally, the proposed method, along with the ORB-SLAM2 and ORB-SLAM3 algorithms, was comprehensively verified on the TUM dataset and the dataset of the actual underground coal mine scenes, covering multiple aspects such as image enhancement, feature matching, and localization.
Results and Conclusions The results indicate that on the TUM dataset, the proposed method reduced the root mean square errors (RMSEs) of absolute and relative trajectory errors by 4%‒38.46% and 8.62%‒50%, respectively compared to ORB-SLAM2 and reduced by 0‒61.68% and 3.63%‒47.05%, respectively compared to ORB-SLAM3. Experiments on the actual underground coal mine scenes revealed that the location trajectories of the proposed method were aligned with the reference trajectory of camera motion more closely. Furthermore, the proposed method effectively enhanced the accuracy and robustness of the visual SLAM system in the feature degradation scene in underground coal mines, providing a technical solution for its applications in such settings. Research on visual SLAM methods tailored for feature degradation scenes in underground coal mines holds great significance for advancing the roboticization of mobile equipment used in coal mines.
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