The article is devoted to the development and implementation of a two-stage magnetometer calibration algorithm integrated into the navigation system of a small-class unmanned underwater vehicle. At the first stage, an ellipsoidal approximation method is used to compensate for soft iron and hard iron distortion, ensuring the correct geometric location of magnetometer measurements. The second stage of calibration involves a method for estimating rotation between the coordinate systems of the magnetometer and accelerometer using quaternions as rotation parameters. Experimental verification of the algorithm demonstrated its effectiveness. Following completion of the two-step calibration, calibration parameters were determined and their use confirmed good consistency between magnetometer readings and actual magnetic field data, indicating the feasibility of using this technique for calibrating magnetometers.. The proposed algorithm for two-stage magnetometer calibration does not require laboratory equipment and can be carried out under real-world operating conditions. This makes it possible to integrate it into the onboard software of unmanned underwater vehicles.

Keywords: calibration, magnetometer, accelerometer, MEMS sensor, AHRS, navigation system, unmanned underwater vehicle, ellipsoid approximation, quaternion, magnetic inclination

The article presents an analysis of modern methods and prospects for the application of automated technologies and laser triangulation for visual inspection of weld quality in the production of large-diameter longitudinally welded pipes. A review of scientific and patent publications over the past 5 years was conducted using databases such as Google Scholar, Scopus, Web of Science, eLibrary, and Google Patents. Key aspects such as the use of laser triangulation sensors (hereafter referred to as LTS) for assessing the geometric parameters of welds and the integration of machine learning methods to enhance inspection accuracy and automation were considered. The study shows that the application of LTS in combination with machine learning methods ensures high accuracy in evaluating weld quality, which is crucial for ensuring the reliability of pipelines in various industries. Based on the conducted analysis, recommendations for developing an automated system for visual inspection of welds on production lines have been identified.

Keywords: laser triangulation, visual inspection, welds, automated technologies, machine learning, quality control, large-diameter welded pipes