Motion of moving camera from point matches: comparison of two robust estimation methods
2014 (English)In: IET Computer Vision, ISSN 1751-9632, E-ISSN 1751-9640, Vol. 8, no 6, 682-689 p.Article in journal (Refereed) Published
A robust estimation method, Balanced Least Absolute Value Estimator (BLAVE), is introduced and compared with thetraditional RANdom SAmple Consensus (RANSAC) method. The comparison is performed empirically by applying bothestimators on the camera motion parameters estimation problem. A linearised model for this estimation problem is derived.The tests were performed on a simulated scene with added random noise and gross errors as well as on actual images takenby a mobile mapping system. The greatest advantage of BLAVE is that it processes all observations at once as well as itsmedian-like property: the estimated parameters are not influenced by the size of the outliers. It can tolerate up to 50% outliersin data and still produce accurate results. The greatest disadvantage of RANSAC is that the results are not repeatable becauseof the random sampling of data. Moreover, the results are less accurate, because RANSAC generally does not produce a‘best-fit’ parameter estimation. The number of trials, which must be tested by RANSAC to find a reasonable solution,depends on the portion of outliers in data. The computational time for BLAVE does not depend on the portion of outliers inthe observations, but it grows with the number of observations, same as RANSAC.
Place, publisher, year, edition, pages
Institution of Engineering and Technology, 2014. Vol. 8, no 6, 682-689 p.
Robust estimation, motion of camera, balanced least absolute value estimator
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject Geodesy and Geoinformatics
IdentifiersURN: urn:nbn:se:kth:diva-149639DOI: 10.1049/iet-cvi.2013.0271ISI: 000348194100021ScopusID: 2-s2.0-84915778513OAI: oai:DiVA.org:kth-149639DiVA: diva2:740439
QC 201409022014-08-252014-08-252015-04-23Bibliographically approved