  Unmanned Aerial Vehicles (UAVs) represent a new frontier in a wide range of
monitoring and research applications. To fully leverage their potential, a key
challenge is planning missions for efficient data acquisition in complex
environments. To address this issue, this article introduces a general
Informative Path Planning (IPP) framework for monitoring scenarios using an
aerial robot, focusing on problems in which the value of sensor information is
unevenly distributed in a target area and unknown a priori . The approach is
capable of learning and focusing on regions of interest via adaptation to map
either discrete or continuous variables on the terrain using
variable-resolution data received from probabilistic sensors. During a mission,
the terrain maps built online are used to plan information-rich trajectories in
continuous 3-D space by optimizing initial solutions obtained by a coarse grid
search. Extensive simulations show that our approach is more efficient than
existing methods. We also demonstrate its real-time application on a
photorealistic mapping scenario using a publicly available dataset and
demonstrate a proof of concept for an agricultural monitoring task.
