Close-range photogrammetry methods are widely used for non-contact and accurate measurements of surface shapes. These methods are based on calculating the three-dimensional coordinates of an object from two-dimensional images using special digital processing algorithms. Due to the relatively complex measurement principle, the accurate estimation of the photogrammetric measurement error is a non-trivial task. Typically, theoretical estimations or computer modelling are used to solve this problem. However, these approaches cannot provide an accurate estimate because it is impossible to consider all factors that influence the measurement results. To solve this problem, we propose the use of physical modelling. The measurement results from the photogrammetric system under test were compared with the results of a more accurate reference measurement method. This comparison allowed the error to be estimated under controlled conditions. The test object was a flexible surface whose shape could vary smoothly over a wide range. The estimation of the measurement accuracy for a large number of different surface shapes allows us to obtain new results that are difficult to obtain using standard approaches. To implement the proposed approach, a laboratory system for the error estimation of close-range photogrammetric measurements was developed. The paper contains a detailed description of the developed system and the proposed technique for a comparison of the measurement results. The error in the reference method, which was chosen to be phasogrammetry, was evaluated experimentally. Experimental testing of the stereo photogrammetric system was performed according to the proposed technique. The obtained results show that the proposed technique can reveal dependencies that may not be detected by standard approaches.

Subject of study. This study investigates the influence of optical-system parameters on the error in determining the orientation and position of fiducial markers. Aim of study. This study determines the dependencies of absolute error in position and orientation on various influencing factors. Method. The error in a machine-vision system is assessed based on fiducial markers using computer-image modeling in the Unity 3D graphics system. Main results. Over 100,000 images of AprilTag markers in different positions and orientations were synthesized and processed during the simulation. The results of this simulation yielded the dependencies of absolute position and orientation errors on the distance between the camera and marker, the rotation angle of the marker, and the focal lengths of the camera. Practical significance. The obtained results may be utilized to optimize the placement of markers on the platform, select the optimal video camera positions and lens focal lengths, and implement adjustments in the image-processing algorithm. These changes can improve measurement accuracy in systems used for developing orientation algorithms for microsatellites.

Fiducial markers are widely used in machine vision systems. One of the main ways of their application is to estimate the relative position of the camera and marker in space. The latter are visual artificial objects, which are designed for their robust detection among other objects in the environment of the images. Examples of fiducial marker accuracy estimation are found in many studies. Usually such studies are conducted for one or two or three code marker systems. And accuracy estimates are given only for specific parameters of the machine vision system, which is designed by the authors of the work. This approach is not universal, and for other parameters it is necessary to carry out the error estimation again. This paper presents an approach to determining the error for comparing more fiducial marker systems with each other using more universal metrics. For this purpose, computer simulations of marker images were performed for five different systems with different spatial orientations. The images were processed using detection algorithms proposed by the authors of the marker systems. The processing results were compared with the specified parameters in the simulations. Relative error estimates were obtained for the five different code marker systems.

Close-range photogrammetry is widely used to measure the surface shape of various objects and its deformations. The classic approach for this is to use a stereo pair of images, which are captured from different angles using two digital video cameras. The surface shape is measured by triangulating a set of corresponding two-dimensional points from these images using a predetermined location of cameras relative to each other. Various algorithms are used to find these points. Several photogrammetry methods use cross-correlation for this purpose. This paper discusses the possibility of replacing the correlation algorithm with neural networks to determine displacements of small areas in the images. They allow increasing the calculation speed and the spatial resolution of the measurement results. To verify the possibility of using convolutional networks for photogrammetry tasks, computer and physical modeling were carried out. For the first test, a set of synthetically generated images representing images of the Particle Image Velocimetry method was used. The displacements of particles in the images are known, it allows to estimate the accuracy of processing of such images. For the second test, a series of experimental images with surfaces with different deformation was obtained. Computational experiments were performed to process synthetic and experimental images using selected neural networks and a classical cross-correlation algorithm. The limitations on the use of the compared algorithms were determined and their error in reconstructing the three-dimensional shape of the surface was evaluated. Computer and physical modeling have shown the operability and efficiency of neural networks for processing photogrammetry images.