Purposive sample consensus: A paradigm for model fitting with application to visual odometry

Field	Value	Language
dc.contributor.author	Wang, J https://orcid.org/0000-0002-6282-0456	en_US
dc.contributor.author	Luo, X	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Springer Tracts in Advanced Robotics, 2015, 105 pp. 335 - 349	en_US
dc.identifier.isbn	9783319074870	en_US
dc.identifier.issn	1610-7438	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41579
dc.description.abstract	© Springer International Publishing Switzerland 2015. 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.	en_US
dc.relation.ispartof	Springer Tracts in Advanced Robotics	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Purposive sample consensus: A paradigm for model fitting with application to visual odometry	en_US
dc.type	Conference Proceeding
utslib.citation.volume	105	en_US
utslib.for	090602 Control Systems, Robotics and Automation	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	105	en_US

Abstract:

© Springer International Publishing Switzerland 2015. 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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http://hdl.handle.net/10453/41579