Incremental, orthorectified and loop-independent mosaicking of aerial images taken by micro UAVs

In this paper we survey thoroughly the problem of orthorectified and incremental image mosaicking of a sequence of aerial images taken from low-altitude micro aerial vehicles. Most of existing approaches have been exploiting the global optimization (in presence of a loop in the image sequences) to distribute and/or metadata to mitigate the accumulating stitching error. However, the resulting mosaic can be improved if the errors are diminished by studying their sources. Mostly the UAV aerial image mosaicking is affected by the following three important sources of error: i) a weak homography as a result of using unleveled ground control points (GCPs) for image registration, ii) a poor camera calibration and image rectification, and iii) deficiency of a well-defined projection model (cylindrical, planar, etc) and consequently an inappropriate transformation model. We investigate the influences of using a depth map to find the features from the same plane, geometric distortion correction and combining the appropriate choice of projection and transformation model for the mosaicking. We further quantify the improvement of orthorectification in mosaics by mitigating those errors and demonstrate the improvement on real-world mosaics.