Abstract
Small unmanned air vehicles (UAVs) provide an economical means of imaging large areas of terrain at far lower cost than satellites. Applications range from precision agriculture to disaster response and power line maintenance. Because small UAVs fly at low altitudes of approximately 100 meters, their cameras have only a limited field of view and must take thousands of photographs to cover a reasonably sized area. To provide a unified view of the area, these photographs must be combined into a seamless photo mosaic. The conventional approach for accomplishing this mosaicking process is called block bundle adjustment, and it works well if there are only a few tens or hundreds of photographs. It runs in O(n3) time, where n is the number of images. When there are thousands of photographs, this method fails because its memory and computational time requirements become prohibitively excessive. We have developed a new technique that replaces bundle adjustment with an iterative algorithm that is very fast and requires little memory. After pairwise image registration, the algorithm projects the resulting tie points to the ground and moves them closer to each other to produce a new set of control points. It fits the image parameters to these control points and repeats the process iteratively to convergence. The algorithm is implemented as an image mosaicking application in Java and runs on a Windows PC. It executes in O(n) time and produces very high resolution mosaics (2 cm per pixel) at the rate of 14 sec per image. This time includes all steps of the mosaicking process from the disk read of the imagery to the disk output of the final mosaic. Experiments show the algorithm to be accurate and reliable for mosaicking thousands of images.