Robust-PCA-based hierarchical plane extraction for application to geometric 3D indoor mapping
- Authors
- Yeon, Suyong; Jun, ChangHyun; Choi, Hyunga; Kang, Jaehyeon; Yun, Youngmok; Doh, Nakju Lett
- Issue Date
- 2014
- Publisher
- EMERALD GROUP PUBLISHING LTD
- Keywords
- Hierarchical segmentation; Plane extraction; Plane registration; Robust PCA
- Citation
- INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION, v.41, no.2, pp.203 - 212
- Indexed
- SCIE
SCOPUS
- Journal Title
- INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION
- Volume
- 41
- Number
- 2
- Start Page
- 203
- End Page
- 212
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/101165
- DOI
- 10.1108/IR-04-2013-347
- ISSN
- 0143-991X
- Abstract
- Purpose - The authors aim to propose a novel plane extraction algorithm for geometric 3D indoor mapping with range scan data. Design/methodology/approach - The proposed method utilizes a divide-and-conquer step to efficiently handle huge amounts of point clouds not in a whole group, but in forms of separate sub-groups with similar plane parameters. This method adopts robust principal component analysis to enhance estimation accuracy. Findings - Experimental results verify that the method not only shows enhanced performance in the plane extraction, but also broadens the domain of interest of the plane registration to an information-poor environment (such as simple indoor corridors), while the previous method only adequately works in an information-rich environment (such as a space with many features). Originality/value - The proposed algorithm has three advantages over the current state-of-the-art method in that it is fast, utilizes more inlier sensor data that does not become contaminated by severe sensor noise and extracts more accurate plane parameters.
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Collections - Executive Vice President for Research > Institute of Convergence Science > 1. Journal Articles
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