  The ability to plan informative paths online is essential to robot autonomy.
In particular, sampling-based approaches are often used as they are capable of
using arbitrary information gain formulations. However, they are prone to local
minima, resulting in sub-optimal trajectories, and sometimes do not reach
global coverage. In this paper, we present a new RRT*-inspired online
informative path planning algorithm. Our method continuously expands a single
tree of candidate trajectories and rewires segments to maintain the tree and
refine intermediate trajectories. This allows the algorithm to achieve global
coverage and maximize the utility of a path in a global context, using a single
objective function. We demonstrate the algorithm's capabilities in the
applications of autonomous indoor exploration as well as accurate Truncated
Signed Distance Field (TSDF)-based 3D reconstruction on-board a Micro Aerial
vehicle (MAV). We study the impact of commonly used information gain and cost
formulations in these scenarios and propose a novel TSDF-based 3D
reconstruction gain and cost-utility formulation. Detailed evaluation in
realistic simulation environments show that our approach outperforms state of
the art methods in these tasks. Experiments on a real MAV demonstrate the
ability of our method to robustly plan in real-time, exploring an indoor
environment solely with on-board sensing and computation. We make our framework
available for future research.
