Identifying support surfaces of climbable structures from 3D point clouds

Anna Eilering, Victor Yap, Jeff Johnson, Kris Hauser

Research output: Contribution to journalConference articlepeer-review


This paper presents a probabilistic technique for identifying support surfaces like floors, walls, stairs, and rails from unstructured 3D point cloud scans. A Markov random field is employed to model the joint probability of point labels, which can take on a number of user-defined surface classes. The probability of a point depends on both local spatial features of the point cloud around the point as well as the classifications of points in its neighborhood. The training step estimates joint and pairwise potentials from labeled point cloud datasets, and the prediction step aims to maximize the joint probability of all labels using a hill-climbing procedure. The method is applied to stair and ladder detection from noisy and partial scans using three types of sensors: A sweeping laser sensor, time-offlight depth camera, and a Kinect depth camera. The resulting classifier achieves approximately 75% accuracy and is robust to variations in point density.

Original languageEnglish (US)
Article number6907777
Pages (from-to)6226-6231
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
StatePublished - Sep 22 2014
Externally publishedYes
Event2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China
Duration: May 31 2014Jun 7 2014

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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