Scalable extrinsic calibration of omni-directional image networks
We describe a linear-time algorithm that recovers absolute camera orientations and positions, along with uncertainty estimates, for networks of terrestrial image nodes spanning hundreds of meters in outdoor urban scenes. The algorithm produces pose estimates globally consistent to roughly 0.1degrees (2 milliradians) and 5 centimeters on average, or about four pixels of epipolar alignment. We assume that adjacent nodes observe overlapping portions of the scene, and that at least two distinct vanishing points are observed by each node. The algorithm decouples registration into pure rotation and translation stages. The rotation stage aligns nodes to commonly observed scene line directions; the translation stage assigns node positions consistent with locally estimated motion directions, then registers the resulting network to absolute (Earth) coordinates. The paper's principal contributions include: extension of classic registration methods to large scale and dimensional extent; a consistent probabilistic framework for modeling projective uncertainty; and a new hybrid of Hough transform and expectation maximization algorithms. We assess the algorithm's performance on synthetic and real data, and draw several conclusions. First, by fusing thousands of observations the algorithm achieves accurate registration even in the face of significant lighting variations, low-level feature noise, and error in initial pose estimates. Second, the algorithm's robustness and accuracy increase with image field of view. Third, the algorithm surmounts the usual tradeoff between speed and accuracy; it is both faster and more accurate than manual bundle adjustment.
exterior orientation egomotion structure from motion panoramas