Abstract
Monitoring and characterization of rock face is an important aspect of field survey in order to meet the safety requirements for any rock engineering activity. Currently, most of the rock face mapping activities are carried out using manual surveying with compass-clinometers. Laser scanners coupled with their inbuilt commercial software packages provide ways of semi-automated extraction and mapping of rock faces. However, these laser scanner–based solutions are not widely used by geologists, mainly due to the high cost associated with data acquisition. In this research, a digital image-based semi-automatic approach for 3D mapping of rock faces has been proposed. Here, image sequences of geo-objects are used for generating 3D dense point clouds, which are later used for 3D mapping and characterization of rocks using point cloud normal-based clustering algorithm for planar segmentation. The semi-automatic 3D rock face mapping approach proposed here is a robust and cost-effective alternative to the commercial software packages used worldwide, making it the most promising rock face mapping tool for the future.
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Acknowledgements
The authors would like to thank the Indian Institute of Technology, Kharagpur, for providing the research facilities which made this work possible. They are also grateful to Sukinda Chromite Mine, Tata Steel Ltd., for allowing collection of the field data necessary for this research.
Dedication
This research paper is dedicated to the loving memory of late Mrs. Nilima Basu, whose unconditional love and support made this research possible.
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Maiti, A., Choudhary, A. & Chakravarty, D. A k-means clustering–based approach for 3D mapping and characterization of rock faces using digital images. Arab J Geosci 14, 848 (2021). https://doi.org/10.1007/s12517-021-07209-w
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DOI: https://doi.org/10.1007/s12517-021-07209-w