Dynamic Updating of Planar Structure and Motion: The Case of Constant Motion

This paper describes an algorithm which uses the formalism of the extended Kalman Filter to provide a dynamic solution to the nonlinear reconstruction of the 3D structure and motion of a planar facet moving with arbitrary but constant motion relative to a single camera. By integrating measurements of visual motion over time, the algorithm restores the coupling between the scene structure and rotational motion which is absent in instantaneous or “snapshot” processing of visual motion, thereby disambiguating the two possible values of the rotational motion which arise in such processing. The dynamic recovery of planar structure from motion is demonstrated on real imagery in sequences of several tens of frames taken both indoors in a well-controlled environment and outdoors from a moving vehicle.

[1]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[2]  Shahriar Negahdaripour,et al.  Determining 3-D Motion of Planar Objects from Image Brightness Patterns , 1985, IJCAI.

[3]  Thomas S. Huang,et al.  Estimating three-dimensional motion parameters of a rigid planar patch , 1981 .

[4]  Christopher G. Harris,et al.  3D positional integration from image sequences , 1988, Image Vis. Comput..

[5]  H. C. Longuet-Higgins,et al.  The interpretation of a moving retinal image , 1980, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[6]  Hilary Buxton,et al.  Applications of a Fast Parallel Algorithm for the Extraction and Interpretation of Optical Flow , 1986, ECAI.

[7]  R. Chellappa,et al.  Recursive 3-D motion estimation from a monocular image sequence , 1990 .

[8]  H C Longuet-Higgins,et al.  The visual ambiguity of a moving plane , 1984, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[9]  Kenichi Kanatani,et al.  Detecting the motion of a planar surface by line and surface integrals , 1984, Comput. Vis. Graph. Image Process..

[10]  Olivier D. Faugeras,et al.  Structure and motion from two noisy perspective views , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[11]  Roberto Cipolla,et al.  Active Visual Inference of Surface Shape , 1995, Lecture Notes in Computer Science.

[12]  Larry S. Shapiro,et al.  A Matching and Tracking Strategy for Independently Moving Objects , 1992 .

[13]  Jan J. Koenderink,et al.  Local structure of movement parallax of the plane , 1976 .

[14]  Richard Evans.  Kalman filtering of pose estimates in applications of the RAPID video rate tracker , 1990, BMVC.

[15]  David W. Murray,et al.  3D Solutions to the Aperture Problem , 1984, ECAI.

[16]  A. Waxman,et al.  On the Uniqueness of Image Flow Solutions for Planar Surfaces in Motion , 1985 .

[17]  Olivier Faugeras,et al.  Three D-Dynamic Scene Analysis: A Stereo Based Approach , 1992 .

[18]  David W. Murray,et al.  Experiments in the machine interpretation of visual motion , 1990 .