With the development of artificial intelligence technology, visual simultaneous localization and mapping (SLAM) has become a cheap and efficient localization method for underwater robots. However, there are many problems in underwater visual SLAM, such as more serious underwater imaging distortion, more underwater noise, and unclear details. In this paper, we study these two problems and chooses the ORB-SLAM2 algorithm as the method to obtain the motion trajectory of the underwater robot. The causes of radial distortion and tangential distortion of underwater cameras are analyzed, a distortion correction model is constructed, and five distortion correction coefficients are obtained through pool experiments. Comparing the performances of contrast-limited adaptive histogram equalization (CLAHE), median filtering (MF), and dark channel prior (DCP) image enhancement methods in underwater SLAM, it is found that the DCP method has the best image effect evaluation, the largest number of oriented fast and rotated brief (ORB) feature matching, and the highest localization trajectory accuracy. The results show that the ORB-SLAM2 algorithm can effectively locate the underwater robot, and the correct distortion correction coefficient and DCP improve the stability and accuracy of the ORB-SLAM2 algorithm.

Meterage is an important action to ensure the performance of underwater acoustic positioning system, but the commonly used measurement methods are mostly based on the low degrees of freedom (DOF) measurement and control platforms which could not realize the all-round and high efficiency evaluation of this equipment. In this paper, we develop a new metering method base on the conventional standard 4-DOF measurement and control platform to realize the whole exploration space high efficiency and high accuracy measurement of underwater acoustic positioning systems in 6-DOF space. First, a 4-DOF space to 6-DOF space quantity value propagation model is proposed, and a rigorous mathematical expression is established. Next, a measurement procedure is formed based on the building of a standard base-line field, so the channel of transforming measurable position coordinate of the measured field point in 4-DOF space to the 6-DOF space is established. Finally, the evaluation of the measurement uncertainty is done using both the Guide to the expression of uncertainty in measurement method (GUM method) and Adaptive Monte Carlo method (AMCM method), and the evaluation results of GUM method are verified by AMCM method correspondingly, the effectiveness of this metering theory and the corresponding uncertainty evaluation methods are also illustrated by analyzing the length of coverage interval and numerical tolerances of measurement uncertainty. The research results show that the relative combined uncertainty of the metering system is not higher than 0.18% with the parameters given in this paper.

A four-part diagram illustrating the leak noise filtering method. The top left shows the raw input signal and its spectrogram. An arrow points down to the MFB matching process, displaying the power spectral density and cumulative energy graphs. An arrow points right to the desired signal generated via band-pass filtering. A final arrow points up to the clean output signal and spectrogram processed through the VSS-LMS adaptive filter. Leak noise in water supply pipelines is a complex random signal, hindering the application of adaptive filters like least mean square (LMS) which typically require a known desired signal. This paper proposes a novel filtering method based on main frequency-band (MFB) matching and a variable step-size (VSS)-LMS algorithm. The method utilizes power spectral density (PSD) to adaptively identify the leak frequency band and generate the requisite desired signal. Additionally, VSS-LMS balances convergence speed with steady-state error through dynamic step-size adjustment. Simulations and field experiments demonstrate that the proposed method outperforms wavelet denoising, empirical mode decomposition, and singular value decomposition in noise suppression and convergence speed. It effectively extracts leak noise under strong environmental interference and varying signal-to-noise ratios. This method enhances leak detection accuracy and offers significant practical value for smart water distribution networks.