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An efficient RANSAC hypothesis evaluation using sufficient statistics for RGB-D pose estimation

Version 2 2024-06-06, 03:38
Version 1 2019-06-06, 18:53
journal contribution
posted on 2024-06-06, 03:38 authored by I Senthooran, Manzur MurshedManzur Murshed, Jan Carlo BarcaJan Carlo Barca, J Kamruzzaman, H Chung
Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times.

History

Journal

Autonomous robots

Volume

43

Pagination

1257-1270

Location

New York, N.Y.

ISSN

0929-5593

eISSN

1573-7527

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2018, Springer Science+Business Media, LLC, part of Springer Nature

Issue

5

Publisher

Springer