Estimation of the measurement error of photogrammetric techniques by controlled flexible deformable surface

Ivanova, Yulia V.; Poroykov, A. Yu.

doi:10.1109/reepe.2019.8708779

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…To implement the described physical modelling, a measurement system [ 36 ] consisting of a deformable surface imitator and an optical distance sensor was previously developed ( Figure 1 a). The laser sensor was measured locally at a single point.…”

Section: Methodsmentioning

confidence: 99%

An Error Estimation System for Close-Range Photogrammetric Systems and Algorithms

Poroykov,

Pechinskaya,

Shmatko

et al. 2023

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%

An Error Estimation System for Close-Range Photogrammetric Systems and Algorithms

Poroykov,

Pechinskaya,

Shmatko

et al. 2023

Sensors

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show abstract

“…To evaluate the results of processing by the compared algorithms with physical modeling, 150 pairs of experimental images of the surface with different deformations were used. The images were obtained using the imitator of deformable surface (IDS) described in [24]. The IDS allows to arbitrarily set the shape of the surface by means of digital servo-machines.…”

Section: Physical Modelingmentioning

confidence: 99%

Computer and Physical Modeling for the Estimation of the Possibility of Application of Convolutional Neural Networks in Close-Range Photogrammetry

Pinchukov

Poroykov

Shmatko

et al. 2023

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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.

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“…For experimental research, a test installation described in [10][11] was It allows to set a random three-dimensional surface and measure its profile using a laser distance sensor with an accuracy of 75 µm. Two Basler piA2400-17gm cameras with the lenses Fujinon HF25HA-1B are used to register images.…”

Section: Physical Modelingmentioning

confidence: 99%

Computer and Physical Modeling for Verification of the Stereo System Calibration Parameters Optimization Results

Pinchukov

Shmatko

Poroykov

et al. 2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

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Close-range photogrammetry is widely used to measure surface shapes and diagnose deformation. Usually, a stereo system of video cameras is used to register images of the measured object from several different angles. The surface shape is determined by triangulating a set of 2D points from these images. Triangulation uses the stereo system calibration parameters, which are determined before the experiment. Measurements during conditions with increased vibration loads can lead to a change in the relative position of the cameras of the stereo system (decalibration). This leads to a change in the actual calibration parameters and an increase in the measurement error. The decalibration problem can be solved using multidimensional optimization algorithms. To verify their calculation's results it is proposed to use a computer and physical modeling of decalibration of a video camera stereo system in laboratory conditions. The paper presents the implementation of the optimizing algorithm for the external parameters of a stereo system and the results of its performance during the experimental investigations.

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Estimation of the measurement error of photogrammetric techniques by controlled flexible deformable surface

Cited by 5 publications

References 9 publications

An Error Estimation System for Close-Range Photogrammetric Systems and Algorithms

An Error Estimation System for Close-Range Photogrammetric Systems and Algorithms

Computer and Physical Modeling for the Estimation of the Possibility of Application of Convolutional Neural Networks in Close-Range Photogrammetry

Computer and Physical Modeling for Verification of the Stereo System Calibration Parameters Optimization Results

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