Linear Noise Filtering in a Point Cloud Obtained by Photogrammetric Methods

In geodetic surveys, when using modern methods of collecting geospatial data, an important stage is to ensure sufficient accuracy, as well as reduce possible errors in the obtained data. In photogrammetric processing tasks when creating point clouds, there is a high degree of susceptibility of the resulting data to “noise” - the appearance of spurious points whose position in space does not correspond to the real geometry of the object under study. This occurs due to the technical features of the equipment used, image processing and compression algorithms, photogrammetric image processing algorithms, as well as due to the features of the scene being filmed. This problem is especially relevant when using photogrammetric methods to obtain data from the results of photographing objects with a monotonous surface color without a pronounced texture, including in the winter season with a high degree of snow cover, when it is difficult to find connecting points and compare images. The author considers the possibility of using filters based on statistical parameters of noise distribution in a point cloud to reduce the noise of the obtained data using the example of photogrammetric shooting of samples with varying degrees of texture opacity. It is shown that the statistical distribution of noise under the specified shooting conditions for the analyzed samples in the general case does not correspond to a single type of statistical distribution with predictable parameters. The use of the Kalman filter to the obtained data is shown, its qualitative characteristics of application are determined, and a quantitative assessment of the effect of its application is made.

P.Yu. VOROBIEV, Teacher, Junior Researcher (This email address is being protected from spambots. You need JavaScript enabled to view it.)

National Research Moscow State University of Civil Engineering (26, Yaroslavskoe Highway, Moscow, 129337, Russian Federation)

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