License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.10371.3
URN: urn:nbn:de:0030-drops-29500
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2011/2950/
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Scaramuzza, Davide ; Fraundorfer, Friedrich ; Siegwart, Roland

Real-Time Monocular Visual Odometry for On-Road Vehicles with 1-Point RANSAC

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10371.ScaramuzzaDavide.Paper.2950.pdf (0.5 MB)

Abstract

The first biggest problem in visual motion estimation is data association; matched points contain many outliers that must be detected and removed for the motion to be accurately estimated. In the last few years, a very established method for removing outliers has been the "5-point RANSAC" algorithm which needs a minimum of 5 point correspondences to estimate the model hypotheses. Because of this, however, it can require up to thousand iterations to find a set of points free of outliers. In this talk, I will show that by exploiting the non-holonomic constraints of wheeled vehicles (e.g. cars, bikes, mobile robots) it is possible to use a restrictive motion model which allows us to parameterize the motion with only 1 point correspondence. Using a single feature correspondence for motion estimation is the lowest model parameterization possible and results in the most efficient algorithm for removing outliers: 1-point RANSAC.
The second problem in monocular visual odometry is the estimation of the absolute scale. I will show that vehicle non-holonomic constraints make it also possible to estimate the absolute scale completely automatically whenever the vehicle turns.
In this talk, I will give a mathematical derivation and provide experimental results on both simulated and real data over a large image dataset collected during a 25 Km path.

BibTeX - Entry

@InProceedings{scaramuzza_et_al:DagSemProc.10371.3,
  author =	{Scaramuzza, Davide and Fraundorfer, Friedrich and Siegwart, Roland},
  title =	{{Real-Time Monocular Visual Odometry for On-Road Vehicles with 1-Point RANSAC}},
  booktitle =	{Dynamic Maps},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2011},
  volume =	{10371},
  editor =	{Claus Brenner and Wolfram Burgard and Marc Pollefeys and Christoph Stiller},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2011/2950},
  URN =		{urn:nbn:de:0030-drops-29500},
  doi =		{10.4230/DagSemProc.10371.3},
  annote =	{Keywords: Structure from motion, visual odometry, SLAM, RANSAC, motion constraints}
}

Keywords: Structure from motion, visual odometry, SLAM, RANSAC, motion constraints
Collection: 10371 - Dynamic Maps
Issue Date: 2011
Date of publication: 23.02.2011

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