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Motion and Structure from Motion in a Piecewise Planar Environment

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Abstract

We show in this article that when the environment is piecewise linear, it provides a powerful constraint on the kind of matches that exist between two images of the scene when the camera motion is unknown. For points and lines located in the same plane, the correspondence between the two cameras is a collineation. We show that the unknowns (the camera motion and the plane equation) can be recovered, in general, from an estimate of the matrix of this collineation. The two-fold ambiguity that remains can be removed by looking at a second plane, by taking a third view of the same plane, or by using a priori knowledge about the geometry of the plane being looked at. We then show how to combine the estimation of the matrix of collineation and the obtaining of point and line matches between the two images, by a strategy of Hypothesis Prediction and Testing guided by a Kalman filter. We finally show how our approach can be used to calibrate a system of cameras.
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
inria-00075698, version 1 - 24 May 2006
... This equation was referred to as the C-DTM constraint, as it combines correspondence with a DTM model. The above derivation is simpler and similar to the work of Faugers and Lustman (1988). The C-DTM includes all of the variables in the pose (absolute) and motion (relative) problem, and it has been shown that by extracting a (large) number of feature points and formulating a collection of such equations, all of the unknown variables can be computed. ...
... Speeded Up Robust Features (SURF) [7], a modified Scale Invariant Feature Transform (SIFT) algorithm, is utilized to detect similar features of two frames and then the homography matrix is generated by matching the image features. Given the homography estimated by SURF points, 3-by-1 transition displacement T 0 and normal vector n 0 , can be computed numerically [8]. Then initial homography becomes ...
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... In order to compute the relative pose between two views it is necessary to assume that the scene is locally planar [19], so that the homography can be computed [20], or compute the essential matrix, which can model both planar and general scenes using the five-point algorithm [21]. However, in most cases, a relatively large number of matches between image pairs is required in order to obtain reliable solutions. ...
Preprint
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