A novel algorithm for hierarchical multi-level image mosaicing for autonomous navigation of UAV is proposed.
The main contribution of the proposed system is the blocking of the error accumulation propagated along
the frames, by incrementally building a long-duration mosaic on the fly which is hierarchically composed of
short-duration mosaics. The proposed algorithm fulfills the real-time processing requirements in autonomous
navigation as follows. 1) Causality: the current output of the mosaicing system depends only on the current
and/or previous input frames, contrary to existing offline mosaic algorithms that depend on future input frames as
well. 2) Learnability: the algorithm autonomously analyzes/learns the scene characteristics. 3) Adaptability: the
system automatically adapts itself to the scene change and chooses the proper methods for feature selection (i.e.,
the fast but unreliable LKT vs. the slow but robust SIFT). The evaluation of our algorithm with the extensive
field test data involving several thousand airborne images shows the significant improvement in processing time,
robustness and accuracy of the proposed algorithm.