  This paper presents an online informative path planning approach for active
information gathering on three-dimensional surfaces using aerial robots. Most
existing works on surface inspection focus on planning a path offline that can
provide full coverage of the surface, which inherently assumes the surface
information is uniformly distributed hence ignoring potential spatial
correlations of the information field. In this paper, we utilize manifold
Gaussian processes (mGPs) with geodesic kernel functions for mapping surface
information fields and plan informative paths online in a receding horizon
manner. Our approach actively plans information-gathering paths based on recent
observations that respect dynamic constraints of the vehicle and a total flight
time budget. We provide planning results for simulated temperature modeling for
simple and complex 3D surface geometries (a cylinder and an aircraft model). We
demonstrate that our informative planning method outperforms traditional
approaches such as 3D coverage planning and random exploration, both in
reconstruction error and information-theoretic metrics. We also show that by
taking spatial correlations of the information field into planning using mGPs,
the information gathering efficiency is significantly improved.

Robotics