  Unmanned aerial vehicles are rapidly gaining popularity in a variety of
environmental monitoring tasks. A key requirement for their autonomous
operation is the ability to perform efficient environmental mapping online,
given limited onboard resources constraining operation time, travel distance,
and computational capacity. To address this, we present an online
adaptive-resolution approach for mapping terrain based on Gaussian Process
fusion. A key aspect of our approach is an integral kernel encoding spatial
correlation over the areas of grid cells, which enables modifying map
resolution while maintaining correlations in a theoretically sound fashion.
This way, we can retain details in areas of interest at higher map resolutions
while compressing information in uninteresting areas at coarser resolutions to
achieve a compact map representation of the environment. We evaluate the
performance of our approach on both synthetic and real-world data. Results show
that our method is more efficient in terms of mapping time and memory
consumption without compromising on map quality. Finally, we integrate our
mapping strategy into an adaptive path planning framework to show that it
facilitates information gathering efficiency in online settings.
