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A novel plane extraction approach using supervised learning

journal contribution
posted on 2023-05-19, 04:56 authored by Siddiqui, JR, Havaei, M, Khatibi, S, Lindley, CA
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.

History

Publication title

Machine Vision and Applications: An International Journal

Volume

24

Issue

6

Pagination

1229-1237

ISSN

0932-8092

Department/School

School of Information and Communication Technology

Publisher

Springer-Verlag

Place of publication

175 Fifth Ave, New York, USA, Ny, 10010

Rights statement

Copyright 2013 Springer-Verlag Berlin Heidelberg

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in engineering

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