Scalable, Absolute Position Recovery for Omni-Directional Image Networks

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 uncontrolled lighting. The algorithm requires no human input or interaction. For real data, it recovers camera pose globally consistent on average to roughly five centimeters, or about four pixels of epipolar alignment. The paper's principal contributions include an extension of Markov chain Monte Carlo estimation techniques to the case of unknown numbers of feature points, unknown occlusion and deocclusion, large scale (thousands of images, and hundreds of thousands of point features), and large 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; a new use of the Hough transform is proposed; and a method for aggregating local baseline constraints into a globally consistent pose set is described.