A low-cost approach for the estimation of rock joint roughness using photogrammetry

To evaluate the possibility of utilizing commercially available devices for making joint roughness estimates, a digital single-lens reflex (DSLR) camera and a smartphone were used to reconstruct the three-dimensional models of 10 rock-joint specimens. The accuracy and efficiency were considered in investigating the performance of photogrammetry on the point clouds production and joint roughness estimation in the laboratory. Additionally, a handheld laser scanner was used in collecting high-resolution point clouds of the same specimens as reference data for evaluating the photogrammetry measurement accuracy. The interval for image capture was set at 8? with consideration for the data quality, point density, and computation time. The iterative closest point (ICP) algorithm was used to compare the surface topographies of the rock joints produced via photogrammetry and laser scanning. The point clouds generated by photogrammetry agreed with the laser-scanning results, with a small average difference (0.005 mm for the DSLR camera and 0.006 mm for the smartphone). Furthermore, two -and three-dimensional surface parameters were calculated using the three different sources of point clouds, which were in good agreement in the joint roughness estimates when comparing photogrammetry and laser scanning results. Comparative analyses revealed that both DSLR camera photogrammetry and smartphone photogrammetry can produce accurate point clouds of rock joints for roughness assessment; however, point clouds generated by the DSLR camera had higher accuracies than those generated by the smartphone.

Automated summary

The possibility of using commercially available devices for joint roughness estimates was evaluated in this study. A DSLR camera and a smartphone were used to reconstruct three-dimensional models of rock-joint specimens and photogrammetry was used to estimate joint roughness. The results showed that both the DSLR camera and the smartphone can produce accurate point clouds for roughness assessment, with the DSLR camera having higher accuracy.