EFFICIENT ORIENTATION AND CALIBRATION OF LARGE AERIAL BLOCKS OF MULTI-CAMERA PLATFORMS

Aerial multi-camera platforms typically incorporate a nadir-looking camera accompanied by further cameras that provide oblique views, potentially resulting in utmost coverage, redundancy, and accuracy even on vertical surfaces. However, issues have remained unresolved with the orientation and calibration of the resulting imagery, to two of which we present feasible solutions. First, as standard feature point descriptors used for the automated matching of homologous points are only invariant to the geometric variations of translation, rotation, and scale, they are not invariant to general changes in perspective. While the deviations from local 2D-similarity transforms may be negligible for corresponding surface patches in vertical views of flat land, they become evident at vertical surfaces, and in oblique views in general. Usage of such similarity-invariant descriptors thus limits the amount of tie points that stabilize the orientation and calibration of oblique views and cameras. To alleviate this problem, we present the positive impact on image connectivity of using a quasi affine-invariant descriptor. Second, no matter which hard- and software are used, at some point, the number of unknowns of a bundle block may be too large to be handled. With multi-camera platforms, these limits are reached even sooner. Adjustment of sub-blocks is sub-optimal, as it complicates data management, and hinders self-calibration. Simply discarding unreliable tie points of low manifold is not an option either, because these points are needed at the block borders and in poorly textured areas. As a remedy, we present a straight-forward method how to considerably reduce the number of tie points and hence unknowns before bundle block adjustment, while preserving orientation and calibration quality