A new approach for semi-automatic rock mass joints recognition from 3D point clouds

9 Rock mass characterization requires a deep geometric understanding of the 10 discontinuity sets affecting rock exposures. Recent advances in Light Detection and 11 Ranging (LiDAR) instrumentation currently allow quick and accurate 3D data 12 acquisition, yielding on the development of new methodologies for the automatic 13 characterization of rock mass discontinuities. This paper presents a methodology for the 14 identification and analysis of flat surfaces outcropping in a rocky slope using the 3D 15 data obtained with LiDAR. This method identifies and defines the algebraic equations 16 of the different planes of the rock slope surface by applying an analysis based on a 17 neighbouring points coplanarity test, finding principal orientations by Kernel Density 18 Estimation and identifying clusters by the Density-Based Scan Algorithm with Noise . 19 Different sources of information —synthetic and 3D scanned data— were employed, 20 performing a complete sensitivity analysis of the parameters in order to identify the 21 optimal value of the variables of the proposed method. In addition, raw source files and 22 obtained results are freely provided in order to allow to a more straightforward method 23 comparison aiming to a more reproducible research. 24 25