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Structure from Motion using the Extended Kalman Filter

  • Javier Civera
  • Andrew J. Davison
  • José María Martínez Montiel

Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 75)

Table of contents

  1. Front Matter
    Pages 1-13
  2. Javier Civera, Andrew J. Davison, José María Martínez Montiel
    Pages 1-12
  3. Javier Civera, Andrew J. Davison, José María Martínez Montiel
    Pages 13-32
  4. Javier Civera, Andrew J. Davison, José María Martínez Montiel
    Pages 33-63
  5. Javier Civera, Andrew J. Davison, José María Martínez Montiel
    Pages 65-97
  6. Javier Civera, Andrew J. Davison, José María Martínez Montiel
    Pages 99-110
  7. Javier Civera, Andrew J. Davison, José María Martínez Montiel
    Pages 111-122
  8. Javier Civera, Andrew J. Davison, José María Martínez Montiel
    Pages 123-125
  9. Back Matter
    Pages 1-43

About this book

Introduction

The fully automated estimation of the 6 degrees of freedom camera motion and the imaged 3D scenario using as the only input the pictures taken by the camera has been a long term aim in the computer vision community. The associated line of research has been known as Structure from Motion (SfM). An intense research effort during the latest decades has produced spectacular advances; the topic has reached a consistent state of maturity and most of its aspects are well known nowadays. 3D vision has immediate applications in many and diverse fields like robotics, videogames and augmented reality; and technological transfer is starting to be a reality.

This book describes one of the first systems for sparse point-based 3D reconstruction and egomotion estimation from an image sequence; able to run in real-time at video frame rate and assuming quite weak prior knowledge about camera calibration, motion or scene. Its chapters unify the current perspectives of the robotics and computer vision communities on the 3D vision topic: As usual in robotics sensing, the explicit estimation and propagation of the uncertainty hold a central role in the sequential video processing and is shown to boost the efficiency and performance of the 3D estimation. On the other hand, some of the most relevant topics discussed in SfM by the computer vision scientists are addressed under this probabilistic filtering scheme; namely projective models, spurious rejection, model selection and self-calibration.

Keywords

Computer Vision Robotics SLAM Structure from Motion

Authors and affiliations

  • Javier Civera
    • 1
  • Andrew J. Davison
    • 2
  • José María Martínez Montiel
    • 3
  1. 1., Instituto Universitario de InvestigaciónUniversidad de ZaragozaZaragozaSpain
  2. 2., Department of ComputingImperial CollegeLondonUnited Kingdom
  3. 3., Instituto Universitario de InvestigaciónUniversidad de ZaragozaZaragozaSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-24834-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-24833-7
  • Online ISBN 978-3-642-24834-4
  • Series Print ISSN 1610-7438
  • Series Online ISSN 1610-742X
  • Buy this book on publisher's site