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.
Original language | English |
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Article number | 17107508 |
Pages (from-to) | 203-212 |
Number of pages | 10 |
Journal | Industrial Robot |
Volume | 41 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2014 |
Keywords
- Hierarchical segmentation
- Plane extraction
- Plane registration
- Robust PCA
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Industrial and Manufacturing Engineering