Egomotion estimation with large field-of-view vision
Date
2010
Authors
Lim, John Jin Keat
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This thesis investigates the problem of egomotion estimation in a monocular, large Field-of-View (FOV) camera from two views of the scene. Our focus is on developing new constraints and algorithms that exploit the larger information content of wide FOV images and the geometry of image spheres in order to aid and simplify the egomotion recovery task. We will consider both the scenario of small or differential camera motions, as well as the more general case of discrete camera motions. Beginning with the equations relating differential camera egomotion and optical flow, we show that the directions of flow measured at antipodal points on the image sphere will constrain the directions of egomotion to some subset region on the sphere. By considering the flow at many such antipodal point pairs, it is shown that the intersection of all subset regions arising from each pair yields an estimate of the directions of motion. These constraints were used in an algorithm that performs Hough-reminiscent voting in 2-dimensions to robustly recover motion. Furthermore, we showed that by summing the optical flow vectors at antipodal points, the camera translation may be constrained to lie on a plane. Two or more pairs of antipodal points will then give multiple such planes, and their intersection gives some estimate of the translation direction (rotation may be recovered via a second step). We demonstrate the use of our constraints with two robust and practical algorithms, one based on the RANSAC sampling strategy, and one based on Hough-like voting. The main drawback of the previous two approaches was that they were limited to scenarios where camera motions were small. For estimating larger, discrete camera motions, a different formulation of the problem is required. To this end, we introduce the antipodal-epipolar constraints on relative camera motion. By using antipodal points, the translational and rotational motions of a camera are geometrically decoupled, allowing them to be separately estimated as two problems in smaller dimensions. Two robust algorithms, based on RANSAC and Hough voting, are proposed to demonstrate these constraints. Experiments demonstrated that our constraints and algorithms work competitively with the state-of-the-art in noisy simulations and on real image sequences, with the advantage of improved robustness to outlier noise in the data. Furthermore, by breaking up the problem and solving them separately, more efficient algorithms were possible, leading to reduced sampling time for the RANSAC based schemes, and the development of efficient Hough voting algorithms which perform in constant time under increasing outlier probabilities. In addition to these contributions, we also investigated the problem of 'relaxed egomotion', where the accuracy of estimates is traded off for speed and less demanding computational requirements. We show that estimates which are inaccurate but still robust to outliers are of practical use as long as measurable bounds on the maximum error are maintained. In the context of the visual homing problem, we demonstrate algorithms that give coarse estimates of translation, but which still result in provably successful homing. Experiments involving simulations and homing in real robots demonstrated the robust performance of these methods in noisy, outlier-prone conditions.
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Thesis (PhD)
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