Curtin University Homepage
  • Library
  • FAQ
    • Log in

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Algebraic method to speed up robust algorithms: example of laser-scanned point clouds

    Access Status
    Fulltext not available
    Authors
    Paláncz, B.
    Awange, Joseph
    Lovas, T.
    Lewis, R.
    Molnár, B.
    Heck, B.
    Fukuda, Y.
    Date
    2016
    Collection
    • Curtin Research Publications
    Type
    Journal Article
    Metadata
    Show full item record
    Abstract

    Surface reconstruction from point clouds generated by laser scanning technology has become a fundamental task in many fields of geosciences, such as robotics, computer vision, digital photogrammetry, computational geometry, digital building modelling, forest planning and operational activities. Point clouds produced by laser scanning, however, are limited due to the occurrence of occlusions, multiple reflectance and noise, and off-surface points (outliers), thus necessitating the need for robust fitting techniques. In this contribution, a fast, non-iterative and data invariant algebraic algorithm with constant O(1) complexity that fits planes to point clouds in the total least squares sense using Gaussian-type error distribution is proposed. The maximum likelihood estimator method is used, resulting in a multivariate polynomial system that is solved in an algebraic way. It is shown that for plane fitting when datasets are affected heavily by outliers, the proposed algebraic method can be embedded into the framework of robust methods like the Danish or the RANdom SAmple Consensus methods and computed in parallel to provide rigorous algebraic fitting with significantly reduced running times. Compared to the embedded traditional singular value decomposition and principal component analysis approaches, the performance of the proposed algebraic algorithm demonstrated its efficiency on both synthetic data and real laser-scanned measurements.The evaluation of a symbolic algebraic formula is practically independent of the values of its coefficients; however, the computation of the coefficients depends on the complexity of the data. Since the main advantage of the symbolic solution is its non-requirement of numerical iteration, the data complexity will have weak influence on the speed-up. The novelty of the proposed method is the use of algebraic technique in a robust plane fitting algorithm that could be applied to remote sensing data analysis/delineation/classification. In general, the method could be applied to most plane fitting problems in the geoscience field.

    Citation
    Paláncz, B. and Awange, J. and Lovas, T. and Lewis, R. and Molnár, B. and Heck, B. and Fukuda, Y. 2016. Algebraic method to speed up robust algorithms: example of laser-scanned point clouds. Survey Review. 49 (357): pp. 408-418.
    Source Title
    Survey Review
    URI
    http://hdl.handle.net/20.500.11937/21903
    DOI
    10.1080/00396265.2016.1183939
    Department
    Department of Spatial Sciences

    Related items

    Showing items related by title, author, creator and subject.

    • Robust statistical approaches for local planar surface fitting in 3D laser scanning data
      Nurunnabi, A.; Belton, David; West, Geoff (2014)
      This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis ...
    • Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data
      Nurunnabi, A.; West, Geoff; Belton, David (2015)
      This paper proposes two robust statistical techniques for outlier detection and robust saliency features, such as surface normal and curvature, estimation in laser scanning 3D point cloud data. One is based on a robust ...
    • Diagnostic-robust statistical analysis for Local Surface Fitting in 3D Point Cloud Data
      Nurunnabi, Abdul; Belton, David; West, Geoff (2012)
      Objectives: Surface reconstruction and fitting for geometric primitives and three Dimensional (3D) modeling is a fundamental task in the field of photogrammetry and reverse engineering. However it is impractical to get ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorsTitlesSubjectsDocument TypesThis CollectionIssue DateAuthorsTitlesSubjectsDocument Types

    My Account

    Log in

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Connect with Curtin

    • 
    • 
    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Send FeedbackContact Us
    DSpace software copyright © 2002-2015  DuraSpace