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Field and Service Robotics pp 335–349Cite as

Purposive Sample Consensus: A Paradigm for Model Fitting with Application to Visual Odometry

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Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 105))

Abstract

ANSAC (random sample consensus) is a robust algorithm for model fitting and outliers’ removal, however, it is neither efficient nor reliable enough to meet the requirement of many applications where time and precision is critical. Various algorithms have been developed to improve its performance for model fitting.

A new algorithm named PURSAC (purposive sample consensus) is introduced in this paper, which has three major steps to address the limitations of RANSAC and its variants. Firstly, instead of assuming all the samples have a same probability to be inliers, PURSAC seeks their differences and purposively selects sample sets. Secondly, as sampling noise always exists; the selection is also according to the sensitivity analysis of a model against the noise. The final step is to apply a local optimization for further improving its model fitting performance. Tests show that PURSAC can achieve very high model fitting certainty with a small number of iterations.

Two cases are investigated for PURSAC implementation. It is applied to line fitting to explain its principles, and then to feature based visual odometry, which requires efficient, robust and precise model fitting. Experimental results demonstrate that PURSAC improves the accuracy and efficiency of fundamental matrix estimation dramatically, resulting in a precise and fast visual odometry.

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Authors and Affiliations

  1. University of Technology Sydney (UTS), Sydney, Australia

    Jianguo Wang & Xiang Luo

Authors
  1. Jianguo Wang
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  2. Xiang Luo
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Correspondence to Jianguo Wang .

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Editors and Affiliations

  1. Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia

    Luis Mejias

  2. Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia

    Peter Corke

  3. Autonomous Systems Laboratory QCAT Technology Court, CSIRO ICT Centre, Pullenvale, Queensland, Australia

    Jonathan Roberts

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Wang, J., Luo, X. (2015). Purposive Sample Consensus: A Paradigm for Model Fitting with Application to Visual Odometry. In: Mejias, L., Corke, P., Roberts, J. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-319-07488-7_23

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  • DOI: https://doi.org/10.1007/978-3-319-07488-7_23

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07487-0

  • Online ISBN: 978-3-319-07488-7

  • eBook Packages: EngineeringEngineering (R0)

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