An Underwater Multisensor Fusion Simultaneous Localization and Mapping System Based on Image Enhancement

Abstract

As a key method of ocean exploration, the positioning accuracy of autonomous underwater vehicles (AUVs) directly influences the success of subsequent missions. This study aims to develop a novel method to address the low accuracy in visual simultaneous localization and mapping (SLAM) within underwater environments, enhancing its application in the navigation and localization of AUVs. We propose an underwater multisensor fusion SLAM system based on image enhancement. First, we integrate hybrid attention mechanisms with generative adversarial networks to address the blurring and low contrast in underwater images, thereby increasing the number of feature points. Next, we develop an underwater feature-matching algorithm based on a local matcher to solve the feature tracking problem caused by grayscale changes in the enhanced image. Finally, we tightly couple the Doppler velocity log (DVL) with the SLAM algorithm to better adapt to underwater environments. The experiments demonstrate that, compared to other algorithms, our proposed method achieves reductions in both mean absolute error (MAE) and standard deviation (STD) by up to 68.18% and 44.44%, respectively, when all algorithms are operating normally. Additionally, the MAE and STD of our algorithm are 0.84 m and 0.48 m, respectively, when other algorithms fail to operate properly.

Keywords

• underwater visual SLAM
• multisensor fusion
• image enhancement
• hybrid attention