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Journal Articles

A Learning data collection using a simulator for point cloud based identification system

Tanifuji, Yuta; Kawabata, Kuniaki

Proceedings of International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2020) (Internet), p.246 - 249, 2020/02

Journal Articles

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

 Times Cited Count:1 Percentile:16.64(Computer Science, Artificial Intelligence)

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.

Journal Articles

Development of a GUI-based operation system for building a 3D point cloud classifier

Tanifuji, Yuta; Kawabata, Kuniaki; Hanari, Toshihide

Proceedings of 2019 IEEE Region Ten Conference (TENCON 2019) (Internet), p.36 - 40, 2019/10

Journal Articles

A Structure discrimination method by deep learning with point cloud data

Tanifuji, Yuta; Kawabata, Kuniaki

Proceedings of International Topical Workshop on Fukushima Decommissioning Research (FDR 2019) (Internet), 4 Pages, 2019/05

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