6 October 1994 Recursive estimation for visual navigation
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Proceedings Volume 2350, Videometrics III; (1994); doi: 10.1117/12.189145
Event: Photonics for Industrial Applications, 1994, Boston, MA, United States
Abstract
The paper presents an investigation into 3D structure and motion estimation from image sequences. The concept of a variable dimension 3D Kalman filter is outlined in which the structure and motion parameters of two or more images are reconstructed at each time instant. The developed procedure aims at applications in visual navigation. For a motion unit of two images the length of the state vector is restricted to N X 3 coordinates of N tracked natural landmarks and to 12 motion parameters. Even though new points appear in the sequence with each new processed image, a similar number of points leave the field of view. Therefore the length of the state vector is approximately constant (varies only by a small number of points from image to image) and does not depend on the number of images in the sequence. From a navigational point of view this feature of the proposed procedure is most important. A quality check is reported comparing the structure and motion parameters of the presented procedure to the results of a simultaneous bundle adjustment. The results refer to an experiment in which an observer moves through a stationary but unknown environment.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael Hahn, "Recursive estimation for visual navigation", Proc. SPIE 2350, Videometrics III, (6 October 1994); doi: 10.1117/12.189145; https://doi.org/10.1117/12.189145
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KEYWORDS
Image processing

Filtering (signal processing)

3D image processing

Visualization

Motion estimation

Sensors

Image sensors

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