Clutter Removal Algorithm Based on Grid Density with a Recursive Approach for Rockfall Detection in 3D point clouds from a Terrestrial LiDAR Scanner

Abstract

In analyzing 3D point clouds obtained from a terrestrial LiDAR scanner for rockfall detection, a widely-used clutter removal algorithm is Nearest Neighbor Clutter Removal (NNCR). However, there is a critical problem regarding computational complexity of NNCR. Subsequently, we presented a new algorithm for clutter removal based on grid density as a solution to this problem. Nevertheless, the previously proposed method showed that data points were lost. This study proposes a multi-scale grid-density-based method, assuming that the clutter is normally distributed. Outcomes from the experiment indicate that a proposed method could retrieve data points lost in the previous method. The balanced accuracies, recalls, and F-scores of the proposed method were improved by approximately 13, 33, and 17 percent, respectively, compared with the previously proposed method. Also, the proposed method is about 19 times faster than NNCR.