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A novel plane extraction approach using supervised learning

Citation

Siddiqui, JR and Havaei, M and Khatibi, S and Lindley, CA, A novel plane extraction approach using supervised learning, Machine Vision and Applications: An International Journal, 24, (6) pp. 1229-1237. ISSN 0932-8092 (2013) [Refereed Article]

Copyright Statement

Copyright 2013 Springer-Verlag Berlin Heidelberg

DOI: doi:10.1007/s00138-013-0482-4

Abstract

This paper presents a novel approach for the classification of planar surfaces in an unorganized point clouds. A feature-based planner surface detection method is proposed which classifies a point cloud data into planar and non-planar points by learning a classification model from an example set of planes. The algorithm performs segmentation of the scene by applying a graph partitioning approach with improved representation of association among graph nodes. The planarity estimation of the points in a scene segment is then achieved by classifying input points as planar points which satisfy planarity constraint imposed by the learned model. The resultant planes have potential application in solving simultaneous localization and mapping problem for navigation of an unmanned-air vehicle. The proposed method is validated on real and synthetic scenes. The real data consist of five datasets recorded by capturing three-dimensional(3D) point clouds when a RGBD camera is moved in five different indoor scenes. A set of synthetic 3D scenes are constructed containing planar and non-planar structures. The synthetic data are contaminated with Gaussian and random structure noise. The results of the empirical evaluation on both the real and the simulated data suggest that the method provides a generalized solution for plane detection even in the presence of the noise and non-planar objects in the scene. Furthermore, a comparative study has been performed between multiple plane extraction methods.

Item Details

Item Type:Refereed Article
Keywords:UAV navigation, planar surfaces, point cloud, autonomous navigation
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Adaptive Agents and Intelligent Robotics
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Engineering
Author:Lindley, CA (Dr Craig Lindley)
ID Code:116718
Year Published:2013
Web of Science® Times Cited:1
Deposited By:Computing and Information Systems
Deposited On:2017-05-17
Last Modified:2017-10-23
Downloads:0

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