Extraction of Step-Feature Lines in Open-Pit Mines Based on UAV Point-Cloud Data

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 15; p. 5706
• Main Authors: Mao, Yachun, Wang, Hui, Cao, Wang, Fu, Yuwen, Fu, Yanhua, He, Liming, Bao, Nisha
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 30.07.2022; MDPI
• Subjects: Accuracy; Algorithms; Automation; Curve fitting; Discontinuity; gradient edge detection; Methods; Mines; Mining; Neighborhoods; Nodes; Open pit mining; open-pit mines; Production planning; Resampling; step-feature lines; three-dimensional point cloud; Unmanned aerial vehicles
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22155706

Step-feature lines are one of the important geometrical elements for drawing the status quo maps of open-pit mines, and the efficient and accurate automatic extraction and updating of step-feature lines is of great significance for open-pit-mine stripping planning and analysis. In this study, an automatic extraction method of step-feature lines in an open-pit mine based on unmanned-aerial-vehicle (UAV) point-cloud data is proposed. The method is mainly used to solve the key problems, such as low accuracy, local-feature-line loss, and the discontinuity of the step-feature-line extraction method. The method first performs the regular raster resampling of the open-pit-mine cloud based on the MLS algorithm, then extracts the step-feature point set by detecting the elevation-gradient change in the resampled point cloud, further traces the step-feature control nodes by the seed-growth tracking algorithm, and finally generates smooth step-feature lines by fitting the space curve to the step-feature control nodes. The results show that the method effectively improves the accuracy of step-feature-line extraction and solves the problems of local-feature-line loss and discontinuity.