Transform-domain Image Enhancement for Underwater Visual SLAM based on Homomorphic Filter

Underwater visual SLAM algorithms play a vital role in autonomously localizing Autonomous Underwater Vehicles (AUV) in challenging underwater environments. The low-light conditions underwater necessitate the use of artificial lighting sources. However, this can lead to issues such as overexposed or underexposed images, resulting in reduced image contrast and information content. This study introduces a transform-domain image enhancement technique aimed at bolstering the resilience of underwater visual simultaneous localization and mapping (SLAM) systems. Specifically, a homomorphic filtering method is implemented to alleviate the impact of low-frequency lighting variations on images, thereby enhancing overall image clarity and bolstering the performance of visual SLAM systems. The efficacy of this filtering approach is validated through experiments conducted on the AQUALOC dataset. Our findings demonstrate that this method enhances the success rate of visual SLAM tracking, thereby enhancing its utility in AUV localization tasks.