A robust approach to identify roof bolts in 3D point cloud data captured from a mobile laser scanner

Abstract Roof bolts such as rock bolts and cable bolts provide structural support in underground mines. Frequent assessment of these support structures is critical to maintain roof stability and minimise safety risks in underground environments. This study proposes a robust workflow to classify roof bolts in 3D point cloud data and to generate maps of roof bolt density and spacing. The workflow was evaluated for identifying roof bolts in an underground coal mine with suboptimal lighting and global navigation satellite system (GNSS) signals not available. The approach is based on supervised classification using the multi-scale Canupo classifier coupled with a random sample consensus (RANSAC) shape detection algorithm to provide robust roof bolt identification. The issue of sparseness in point cloud data has been addressed through upsampling by using a moving least squares method. The accuracy of roof bolt identification was measured by correct identification of roof bolts (true positives), unidentified roof bolts (false negatives), and falsely identified roof bolts (false positives) using correctness, completeness, and quality metrics. The proposed workflow achieved correct identification of 89.27% of the roof bolts present in the test area. However, considering the false positives and false negatives, the overall quality metric was reduced to 78.54%.