Joint roughness profiling using photogrammetry

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