Multibeam echo sounders (MBESs) enable extensive underwater environment exploration. However, due to weak correlation between adjacent multibeam sonar data and difficulties in inter-frame feature matching, the resulting underwater mapping accuracy frequently falls short of the desired level. To address this issue, this study presents the development of a multibeam data processing system, which includes functionalities for sonar parameter configuration, data storage, and point cloud conversion. Subsequently, an Iterative Extended Kalman Filter (iEKF) algorithm is employed for odometry estimation, facilitating the initial construction of the point cloud map. To further enhance mapping accuracy, we utilize the Generalized Iterative Closest Point (GICP) algorithm for point cloud registration, effectively merging point cloud data collected at different times from the same location. Finally, real-world lake experiments demonstrate that our method achieves an Absolute Trajectory Error (ATE) of 15.10 m and an average local point cloud registration error of 0.97 m. Furthermore, we conduct measurements on various types of artificial targets. The experimental results indicate that the average location error of the targets calculated by our method is 4.62 m, which meets the accuracy requirements for underwater target exploration.

In the field of underwater perception and detection, side-scan sonar (SSS) plays an indispensable role. However, the imaging mechanism of SSS results in slow information acquisition and high complexity, significantly hindering the advancement of downstream data-driven applications. To address this challenge, we designed an SSS image generator based on diffusion models. We developed a data collection system based on Autonomous Underwater Vehicles (AUVs) to achieve stable and rich data collection. For the process of converting acoustic signals into image signals, we established an image compensation method based on nonlinear gain enhancement to ensure the reliability of remote signals. On this basis, we developed the first controllable category SSS image generation algorithm, which can generate specified data for five categories, demonstrating outstanding performance in terms of the Fréchet Inception Distance (FID) and the Inception Score (IS). We further evaluated our image generator in the task of SSS object detection, and our cross-validation experiments showed that the generated images contributed to an average accuracy improvement of approximately 10% in object detection. The experimental results validate the effectiveness of the proposed SSS image generator in generating highly similar sonar images and enhancing detection accuracy, effectively addressing the issue of data scarcity.

This paper proposes DFFA-Net, a novel differential convolutional neural network designed for underwater optical image dehazing. DFFA-Net is obtained by deeply analyzing the factors that affect the quality of underwater images and combining the underwater light propagation characteristics. DFFA-Net introduces a channel differential module that captures the mutual information between the green and blue channels with respect to the red channel. Additionally, a loss function sensitive to RGB color channels is introduced. Experimental results demonstrate that DFFA-Net achieves state-of-the-art performance in terms of quantitative metrics for single-image dehazing within convolutional neural network-based dehazing models. On the widely-used underwater Underwater Image Enhancement Benchmark (UIEB) image dehazing dataset, DFFA-Net achieves a peak signal-to-noise ratio (PSNR) of 24.2631 and a structural similarity index (SSIM) score of 0.9153. Further, we have deployed DFFA-Net on a self-developed Remotely Operated Vehicle (ROV). In a swimming pool environment, DFFA-Net can process hazy images in real time, providing better visual feedback to the operator. The source code has been open sourced.