Proceedings PaperRecursive estimation for visual navigation
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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.