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Springer Tracts in Advanced Robotics

Structure from Motion using the Extended Kalman Filter

Authors: Civera, Javier, Davison, Andrew J., Martínez Montiel, José María

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  • First book presenting filtering techniques to perform 3D estimation from a monocular sequence in real-time
  • Presents a complete system dealing with the main topics in 3D estimation from real images; namely point and camera modelling, feature correspondences and spurious detection, degenerate motion and self-calibration
  • Written by leading experts in the field
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About this book

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.

Reviews

From the reviews:

“This collection of methods and techniques concerns the so-called structure from motion (SfM) problem … . this book addresses the SfM problem as an unsupervised 3D sparse points reconstruction, in particular using the extended Kalman filter. … a good read for researchers and PhD students in computer vision and robotics areas, because it provides an interesting point of view on how to attack and solve the SfM problem.” (Marco Fratarcangeli, ACM Computing Reviews, March, 2013)


Table of contents (7 chapters)

Table of contents (7 chapters)

Buy this book

eBook $99.00
price for USA in USD
  • ISBN 978-3-642-24834-4
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $159.99
price for USA in USD
  • ISBN 978-3-642-24833-7
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
Softcover $129.99
price for USA in USD
  • ISBN 978-3-642-42786-2
  • Free shipping for individuals worldwide
  • Immediate ebook access, if available*, with your print order
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Structure from Motion using the Extended Kalman Filter
Authors
Series Title
Springer Tracts in Advanced Robotics
Series Volume
75
Copyright
2012
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer Berlin Heidelberg
eBook ISBN
978-3-642-24834-4
DOI
10.1007/978-3-642-24834-4
Hardcover ISBN
978-3-642-24833-7
Softcover ISBN
978-3-642-42786-2
Series ISSN
1610-7438
Edition Number
1
Number of Pages
XVI, 172
Topics

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