Abstract
Presently, the deformation extraction of buildings in mining areas using terrestrial laser scanner (TLS) point clouds is performed manually. Automatic deformation extraction holds great significance for building deformation monitoring in mining areas. Therefore, this study proposes an automatic extraction method for building deformation in mining areas using TLS point clouds. The corner points of doors and windows on the wall are considered as key points and the wall deformation in the mining area is extracted with minimal manual intervention. First, the input data were preprocessed, including 2D boundary point cloud acquisition and denoising (using a distance slope filter). Next, the key points were extracted via three steps: boundary line splitting, seed key point clustering, and key point judgment. Finally, the 3D coordinates of the key points and the relationship between the key points of the two phases were established to calculate the deformation value. The results confirmed negligible difference between the deformation value extracted using this method and the real value. Most of the errors were between −5 and 5 mm, and only a few exceeded ±5 mm; however, no error exceeded ±9 mm. The deformation value obtained using this method was almost identical to that obtained using the manual method, and the absolute error between them was below 8 mm. The performance verification of this method showed that the proposed distance slope filter removed the noise points more effectively compared to that of the traditional denoising filters, i.e., statistical and radius filters, establishing its suitability for the complex measurement environment of mining areas. During the automatic extraction of key point coordinates, the root mean square error (RMSE) values were below 2.0 mm; RMSE values during manual extraction were below 7.1 mm. The proposed method demonstrated greater stability than that of the manual extraction method.
Highlights
After mining of an underground coal seam, the rock stratum around the mining space loses its support and gradually begins to move
Reference [11] proposed a tunnel centerline and section extraction method based on fractional calculus, 3D invariant moments, and best-fit ellipse for tunnel monitoring. This method studies a new smoothing template for terrestrial laser scanner (TLS) point cloud denoising, and on this basis, we propose a new method for extracting the tunnel central axis based on 3D invariant moments
OVERVIEW The automatic extraction method in this study considers the corner points of doors and windows on the building wall as the key points and supports the following two types of input data. (1):On the single-sided wall of two phases, the same area containing key points is selected, and the point cloud in the area is taken as the input data. (2): The single-sided wall point cloud of the two phases is directly taken as the input data
Summary
After mining of an underground coal seam, the rock stratum around the mining space loses its support and gradually begins to move. With the continuous expansion of the mining working face, this movement process gradually spreads to the surface, causing serious damage to the surfaces of buildings [1]-[3]. Extracting the deformation of buildings in mining areas to provide a basis for mining damage assessment has always been trending in deformation monitoring [4], [5]. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
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