Estimation of planes of a rock mass in a gallery wall from point cloud data based on MD PSO

Matsuura, Yuto*; Hayano, Akira; Itakura, Kenichi*; Suzuki, Yukinori*

Applied Soft Computing, 84, p.105737_1 - 105737_9, 2019/11

https://doi.org/10.1016/j.asoc.2019.105737

LiDAR (laser imaging detection and ranging) has been developed to obtain a high-resolution point cloud data indicating the detailed 3D shapes of an object. To identify discontinuities in a rock mass of a tunnel gallery wall, it is necessary to approximate the rock mass surface with small planes. Normal vectors of the planes are important to identify discontinuities. We developed an algorithm for estimation of planes based on multi-dimensional particle swarm optimization (MD PSO) from point cloud data. Point cloud data were segmented into bounding boxes and grouped into clusters by MD PSO. Planes were estimated using the least squares method for point cloud data in the respective clusters. The newly developed MD PSO algorithm was evaluated using point cloud data obtained from a gallery wall. Evaluation was carried out in comparison with the previous developed variable-box segmentation (VBS) algorithm. The MD PSO-based algorithm showed a 7% higher accuracy than that of the VBS algorithm.

Identification of discontinuities in a rock mass from a three-dimensional point cloud using an SVM

Matsukawa, Shun*; Itakura, Kenichi*; Hayano, Akira; Matsuura, Yuto*; Suzuki, Yukinori*

no journal, ,