RS-SLAM: RANSAC sampling for visual FastSLAM

Gim Hee Lee*, Friedrich Fraundorfer, Marc Pollefeys

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

In this paper, we present our RS-SLAM algorithm for monocular camera where the proposal distribution is derived from the 5-point RANSAC algorithm and image feature measurement uncertainties instead of using the easily violated constant velocity model. We propose to do another RANSAC sampling within all the inliers that have the best RANSAC score to check for inlier misclassifications in the original correspondences and use all the hypotheses generated from these consensus sets in the proposal distribution. This is to mitigate data association errors (inlier misclassifications) caused by the observation that the consensus set from RANSAC that yields the highest score might not, in practice, contain all the true inliers due to noise on the feature measurements. Hypotheses which are less probable will eventually be eliminated in the particle filter resampling process. We also show in this paper that our monocular approach can be easily extended for stereo camera. Experimental results validate the potential of our approach.

Originalspracheenglisch
TitelIROS'11 - 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics
Seiten1655-1660
Seitenumfang6
DOIs
PublikationsstatusVeröffentlicht - 2011
VeranstaltungInternational Conference on Intelligent Robots and Systems - San Francisco, USA / Vereinigte Staaten
Dauer: 25 Sept. 201130 Sept. 2011

Konferenz

KonferenzInternational Conference on Intelligent Robots and Systems
Land/GebietUSA / Vereinigte Staaten
OrtSan Francisco
Zeitraum25/09/1130/09/11

ASJC Scopus subject areas

  • Steuerungs- und Systemtechnik
  • Software
  • Maschinelles Sehen und Mustererkennung
  • Angewandte Informatik

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