Towards Robust Monocular Visual Simultaneous Localization and Mapping for Underwater Bionic Robotic Fish: An Approach Integrating Image Enhancement and Deep Feature Matching

In recent years, the demand for autonomous navigation of underwater biomimetic robots in complex environments has continued to grow, among which monocular visual simultaneous localization and mapping (SLAM) has emerged as a key technology for achieving lightweight navigation and localization. However, typical underwater illumination conditions, such as low‐light and artificial lighting, significantly degrade image quality and feature observability, thereby severely limiting the robustness and accuracy of existing visual SLAM systems. To address these challenges, this paper proposes a monocular visual SLAM system for bionic robotic fish, termed GSL‐SLAM , built upon the ORB‐SLAM3 framework. The proposed system integrates adaptive underwater image enhancement with learning‐based feature extraction and matching to improve visual usability and feature matching stability under complex illumination conditions. A multi‐threaded system architecture is further adopted to ensure real‐time performance. Extensive experiments conducted on both a laboratory pool dataset and the public AquaticVision dataset demonstrate that the proposed method consistently outperforms ORB‐SLAM3 under various underwater lighting conditions, including natural light, low light, and artificial illumination. Compared with the baseline method, GSL‐SLAM achieves significant improvements in initialization efficiency, map completeness, and localization accuracy, with particularly notable advantages under low‐light and artificial lighting scenarios. Moreover, the system operates stably at approximately 20 Hz on the embedded platform, satisfying the real‐time perception and navigation requirements of the bionic robotic fish. The experimental results indicate that GSL‐SLAM substantially enhances the robustness and practicality of monocular visual SLAM in complex underwater lighting environments, providing an effective visual localization and mapping solution for lightweight autonomous underwater robots.