  Monocular simultaneous localisation and mapping (SLAM) is a cheap and energy
efficient way to enable Unmanned Aerial Vehicles (UAVs) to safely navigate
managed forests and gather data crucial for monitoring tree health. SLAM
research, however, has mostly been conducted in structured human environments,
and as such is poorly adapted to unstructured forests. In this paper, we
compare the performance of state of the art monocular SLAM systems on forest
data and use visual appearance statistics to characterise the differences
between forests and other environments, including a photorealistic simulated
forest. We find that SLAM systems struggle with all but the most
straightforward forest terrain and identify key attributes (lighting changes
and in-scene motion) which distinguish forest scenes from "classic" urban
datasets. These differences offer an insight into what makes forests harder to
map and open the way for targeted improvements. We also demonstrate that even
simulations that look impressive to the human eye can fail to properly reflect
the difficult attributes of the environment they simulate, and provide
suggestions for more closely mimicking natural scenes.
