Joint roughness profiling using photogrammetry

Abstract

We propose an automated camera setup for photogrammetric roughness analysis in the laboratory environment. The developed fast and low-cost automation setup can be very useful for tedious and laborsome manual field logging practices. The photographs are processed in MATLAB to obtain disparity maps. Coding routines for stereo photogrammetry and digital measurements are written in MATLAB. Secondly, 6 effecting factors (projecting an image onto core face, depth of field, brightness, camera-to-object to baseline distance ratio, projected image size and occlusion) influencing noise in roughness depth maps computed by employing stereo photogrammetry are investigated. After deciding the best values that allow the lowest amount of noise, depth maps of 6 core faces are computed. Using the 3D point cloud generated, roughness profile measurements are made. Then, 8 profile measurements are made for each core face, both manually and digitally.The accuracy of the disparity maps has been verified by comparing 48 joint roughness coefficient (JRC) measurements made manually using a profile gauge. It was proved that surface roughness can be measured very fast in millimetric accuracy with an average Root Mean Square Error (RMSE) of 3.50 and Mean Absolute Error (MAE) of 3.02 by the help of the proposed set-up and calibration.

A Design Scenario Approach for Choosing Protection Works against Rockfall Phenomena

“…To be more specific, in the field of Rock Mechanics, such an approach has proven useful in producing digital models of rock masses and rock outcrops, from which geometrical features are to be extracted: i.e., discontinuity spacing, persistence and orientation derived from traces and planes, respectively [2][3][4][5][6][7][8]. It is also possible to extract the roughness of the discontinuity surfaces visible on the model, provided that this has a sufficiently high resolution [9]. Nowadays, the standard tools employed to produce a digital model of a rock face are terrestrial and drone-based photogrammetry, along with laser scanning techniques.…”

Quantifying the Influence of Surface Texture and Shape on Structure from Motion 3D Reconstructions

“…As the accuracy might vary locally with the object surface geometry, metrics that capture spatial information across multiple length scales are of interest when evaluating SfM reconstructions. One way is by estimating an SD value for either each point on the point cloud or in a moving window around each point on the DEM [ 10 , 22 , 46 , 48 , 49 ].This can be used to study, e.g., the effect of local surface height—either absolute value or variation—on the reconstruction accuracy. Another way is power-spectral density (PSD) analysis, which gives a multi-scale quantification of the surface height variation [ 50 , 51 ].…”