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Supervoxel-based Staircase Detection from Range Data
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 Title & Authors
Supervoxel-based Staircase Detection from Range Data
Oh, Ki-Won; Choi, Kang-Sun;
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 Abstract
In this paper, we propose a supervoxel clustering-based staircase extraction algorithm to obtain poses and dimensions of staircases from a point cloud. In order to effectively reduce the candidate points and accelerate supervoxel clustering, large planes in the scene, such as walls, floors, and ceilings, are eliminated while scanning the environment. Next, staircase candidates with small planes are initially estimated using supervoxel clustering. Then, parameter values for the staircases are refined, and higher staircases that remain undetected due to occlusion are predicted and generated virtually. Experimental results show that staircases are detected accurately and predicted successfully.
 Keywords
Staircase;Plane detection;Supervoxel clustering;Prediction;Point cloud;
 Language
English
 Cited by
 References
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T. Tang, W. Lui and W. Li, "Plane-based detection of staircases using inverse depth", in Proc. Of ACRA 2012, Dec. 2012.

2.
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3.
S. Y. An, L. K. Lee and S. Y. Oh, "Line segment-based fast 3D plane extraction using nodding 2D laser range finder", Robotica, pp. 1-24, September 2014.

4.
G. A. Borges and J. J. Aldon, "Line extraction in 2D range images for mobile robotics", Journal of Intelligent and Robotics Systems, Vol. 40, pp. 267-297, Jul. 2004, doi:10.1023/B:JINT.0000038945.55712.65 crossref(new window)