Supervoxel-based Staircase Detection from Range Data

  • Oh, Ki-Won ;
  • Choi, Kang-Sun
  • Received : 2015.12.14
  • Accepted : 2015.12.22
  • Published : 2015.12.31


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.


Staircase;Plane detection;Supervoxel clustering;Prediction;Point cloud


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