Plane Detection and Segmentation in 3D Point Clouds Using a Multiscale Sliding Voxel Approach

SANDOVAL  GALVEZ Jaime Alberto, UENISHI Kazuma, IWAKIRI Munetoshi, TANAKA Kiyoshi

doi:10.11371/aiieej.48.0_65

Plane Detection and Segmentation in 3D Point Clouds Using a Multiscale Sliding Voxel Approach

DOI

SANDOVAL GALVEZ Jaime Alberto

AB.do Corp.
UENISHI Kazuma

Interdisciplinary Graduate School of Science and Technology, Shinshu University
IWAKIRI Munetoshi

Department of Computer Science, National Defense Academy of Japan
TANAKA Kiyoshi

Academic Assembly (Institute of Engineering), Shinshu University

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説明

Plane detection is a core task in 3D point clouds processing with numerous applications such as registration, object recognition, intelligent robotics, and SLAM. However, RANSAC and other methods based on random sampling are negatively affected by the inliers ratio of the point cloud, and the sliding voxel method depends on the voxel size to ensure its precision. Therefore, in this paper, we propose a multi-scale sliding voxel plane detector that can detect and segment the most reliable planes at each scale efficiently.