  Unmanned aerial vehicles (UAVs) can offer timely and cost-effective delivery
of high-quality sensing data. How- ever, deciding when and where to take
measurements in complex environments remains an open challenge. To address this
issue, we introduce a new multiresolution mapping approach for informative path
planning in terrain monitoring using UAVs. Our strategy exploits the spatial
correlation encoded in a Gaussian Process model as a prior for Bayesian data
fusion with probabilistic sensors. This allows us to incorporate
altitude-dependent sensor models for aerial imaging and perform constant-time
measurement updates. The resulting maps are used to plan information-rich
trajectories in continuous 3-D space through a combination of grid search and
evolutionary optimization. We evaluate our framework on the application of
agricultural biomass monitoring. Extensive simulations show that our planner
performs better than existing methods, with mean error reductions of up to 45%
compared to traditional "lawnmower" coverage. We demonstrate proof of concept
using a multirotor to map color in different environments.
