面向水下环境的视觉惯性SLAM前端改进研究OA

To address the large fluctuation in feature point number and insufficient tracking stability of visual-inertial SLAM front-end caused by significant illumination variation and strong light scattering in underwater environments,this paper proposes an improved visual-inertial SLAM front-end method tailored for underwater applications.The proposed method uses the number of feature points as a feedback metric,establishes a scene feature baseline,and dynamically adjusts the parameters of contrast-limited adaptive histogram equalization to achieve adaptive enhancement under complex underwater lighting conditions.In addition,an improved ORB feature extraction strategy combined with a bidirectional optical flow consistency check is employed to reduce mismatched features induced by underwater noise and image degradation.Experiments conducted on the MCUVI underwater visual-inertial dataset demonstrate that the proposed method improves front-end feature tracking stability under various underwater illumination conditions,and achieves higher absolute trajectory accuracy and improved robustness compared with the original VINS-Mono on sequences where the system runs stably.