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Abstract : |
We describe a linear-time algorithm that recovers absolute camera positions for networks of thousands of terrestrial images spanning hundreds of meters, in outdoor urban scenes, under varying lighting conditions. The algorithm requires no human input or interaction. It is robust to up to 80 % outliers for synthetic data. For real data, it recovers camera pose which is globally consistent on average to roughly 0.1 ? and five centimeters, or about four pixels of epipolar alignment, expending a few CPUhours of computation on a 250MHz processor. This paper?s principal contributions include an extension of Monte Carlo Markov Chain estimation techniques to the case of unknown numbers of feature points, unknown occlusion and deocclusion, and large scale (thousands of images, and hundreds of thousands of point features) and dimensional extent (tens of meters of inter-camera baseline, and hundreds of meters of baseline overall). Also, a principled method is given to manage uncertainty on the sphere of directions; a new use of the Hough Transform is proposed; and a method for aggregating local baseline constraints into a globally, |
