The article presents a comprehensive study of a visual-inertial simultaneous localization and mapping (SLAM) algorithm designed for aerial vehicles. The goal of the research is to propose an improvement to the particle filter SLAM system that allows for more accurate and robust navigation of unknown environments. The authors introduce a modification that utilizes a homography matrix decomposition calculated from the camera frame-to-frame relationships. This procedure aims to refine the particle filter proposal distribution of the estimated robot state. In addition, the authors implement a mechanism of calculating a homography matrix from robot displacement, which is utilized to eliminate outliers in the frame-to-frame feature detection procedure. The algorithm is evaluated using simulation and real-world datasets, and the results show that the proposed improvements make the algorithm more accurate and robust. Specifically, the use of homography matrix decomposition allows the algorithm to be more efficient, with a smaller number of particles, without sacrificing accuracy. Furthermore, the incorporation of robot displacement information helps improve the accuracy of the feature detection procedure, leading to more reliable and consistent results. The article concludes with a discussion of the implemented and tested SLAM solution, highlighting its strengths and limitations. Overall, the proposed algorithm is a promising approach for achieving accurate and robust autonomous navigation of unknown environments.

Wind turbines are among the key equipment needed for eco-friendly generation of electricity. Maintaining wind turbines in excellent technical condition is extremely important not only for safety but also for efficient operation. Studies indicate that defects in the external structure of a turbine blade reduce energy production efficiency. This research investigated the potential of the terrestrial laser scanning technology to examine the technical conditions of wind turbine blades. The main aim of the study was to examine whether terrestrial laser scanning measurements can be valuable for wind turbine blade condition surveying. The investigation was based on the radiometric analyses of point clouds, which forms the novelty of the present study. Condition monitoring focuses on the detection of defects, such as cracks, cavities, or signs of erosion. Moreover, this study consisted of two stages. The next objective entailed the development and examination of two different measurement methods. It was then identified which method is more advantageous by analysing their effectiveness and other economic considerations.

Geospatial data obtained using Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) are increasingly used to model the terrain in the coastal zone, in particular in shallow waterbodies (with a depth of up to 1 m). In order to generate a terrain relief, it is important to choose a method for modelling that will allow it to be accurately projected. Therefore, the aim of this article is to present a method for accuracy assessment of topo-bathymetric surface models based on geospatial data recorded by UAV and USV vehicles. Bathymetric and photogrammetric measurements were carried out on the waterbody adjacent to the public beach in Gdynia (Poland) in 2022 using a DJI Phantom 4 RTK UAV and an AutoDron USV. The geospatial data integration process was performed in the Surfer software. As a result, Digital Terrain Models (DTMs) in the coastal zone were developed using the following terrain modelling methods: Inverse Distance to a Power (IDP), Inverse Distance Weighted (IDW), kriging, the Modified Shepard’s Method (MSM) and Natural Neighbour Interpolation (NNI). The conducted study does not clearly indicate any of the methods, as the selection of the method is also affected by the visualization of the generated model. However, having compared the accuracy measures of the charts and models obtained, it was concluded that for this type of data, the kriging (linear model) method was the best. Very good results were also obtained for the NNI method. The lowest value of the Root Mean Square Error (RMSE) (0.030 m) and the lowest value of the Mean Absolute Error (MAE) (0.011 m) were noted for the GRID model interpolated with the kriging (linear model) method. Moreover, the NNI and kriging (linear model) methods obtained the highest coefficient of determination value (0.999). The NNI method has the lowest value of the R68 measure (0.009 m), while the lowest value of the R95 measure (0.033 m) was noted for the kriging (linear model) method.

The article presents the results of research on the development of a method for improving the positioning accuracy of an UAV equipped with a single-frequency GPS receiver for determining the linear elements of exterior orientation in aerial photogrammetry. Thus, the paper presents a computational strategy for improving UAV position determination using the SPP code method and the products of the IGS service. The developed algorithmswere tested in two independent research experiments performed with theUAVplatform on which an AsteRx-m2 UAS single-frequency receiver was installed. As a result of the experiments, it was shown that the use of IGS products in the SPP code method made it possible to improve the accuracy of the linear elements to the level of about ±2.088 m for X coordinate, ±1.547 m for Y coordinate, ±3.712 m for Z coordinate. The paper also shows the trend of changes in the obtained accuracy in determining linear elements of exterior orientation in the form of a linear regression function. Finally, the paper also applies the SBAS corrections model for the improvement of UAV position calculation and determination of linear elements of exterior orientation. In this case, the improvement in the accuracy of determining the linear elements of exterior orientation is about ±1.843 m for X coordinate, ±1.658 m for Y coordinate, ±7.930 m for Z coordinate. As the obtained test results show, the use of IGS products and SBAS corrections in the SPP code method makes it possible to improve the determination ofUAVpositions for the use in aerial photogrammetry. Keywords: UAV, GNSS measurements, linear elements of exterior orientation, accuracy.

In pursuit of increased efficiency and longer operating times of photovoltaic systems, one may encounter numerous difficulties in the form of defects that occur in both individual solar cells and whole modules. The causes of the occurrence range from structural defects to damage during assembly or, finally, wear and tear of the material due to operation. This article provides an overview of modern imaging methods used to detect various types of defects found in photovoltaic cells and panels. The first part reviews typical defects. The second part of the paper reviews imaging methods with examples of the authors’ own test results. The article concludes with recommendations and tables that provide a kind of comprehensive guide to the methods described, depending on the type of defects detected, the range of applicability, etc. The authors also shared their speculations on current trends and the possible path for further development and research in the field of solar cell defect analysis using imaging.

This paper presents a novel measurement method and briefly discusses the basic properties of direction of arrival (DoA) measurement in a multiple-input multiple-output (MIMO) radar system by using orthogonality with time-division multiplexing (TDM), where only one transmitting antenna element is active in each time slot. This paper presents the mathematical model of the TDM-MIMO radar operating at 10 GHz, transmitting a string of pulses, the method of transmitting and receiving the signal, and the method of measuring the angle of arrival of the signal based on the use of the Capon algorithm and its modifications. Finally, the correctness of the theory, algorithm and method of measuring the direction of arrival of the signal is verified by experimental simulation. The work discussed in this paper is of great significance to practically demonstrate the capabilities of the TDM MIMO radar sensor in practical implementations like reconnaissance and electronic warfare systems.