  Forestry constitutes a key element for a sustainable future, while it is
supremely challenging to introduce digital processes to improve efficiency. The
main limitation is the difficulty of obtaining accurate maps at high temporal
and spatial resolution as a basis for informed forestry decision-making, due to
the vast area forests extend over and the sheer number of trees. To address
this challenge, we present an autonomous Micro Aerial Vehicle (MAV) system
which purely relies on cost-effective and light-weight passive visual and
inertial sensors to perform under-canopy autonomous navigation. We leverage
visual-inertial simultaneous localization and mapping (VI-SLAM) for accurate
MAV state estimates and couple it with a volumetric occupancy submapping system
to achieve a scalable mapping framework which can be directly used for path
planning. As opposed to a monolithic map, submaps inherently deal with
inevitable drift and corrections from VI-SLAM, since they move with pose
estimates as they are updated. To ensure the safety of the MAV during
navigation, we also propose a novel reference trajectory anchoring scheme that
moves and deforms the reference trajectory the MAV is tracking upon state
updates from the VI-SLAM system in a consistent way, even upon large changes in
state estimates due to loop-closures. We thoroughly validate our system in both
real and simulated forest environments with high tree densities in excess of
400 trees per hectare and at speeds up to 3 m/s - while not encountering a
single collision or system failure. To the best of our knowledge this is the
first system which achieves this level of performance in such unstructured
environment using low-cost passive visual sensors and fully on-board
computation including VI-SLAM.
