  Forestry is a major industry in many parts of the world. It relies on forest
inventory, which consists of measuring tree attributes. We propose to use 3D
mapping, based on the iterative closest point algorithm, to automatically
measure tree diameters in forests from mobile robot observations. While
previous studies showed the potential for such technology, they lacked a
rigorous analysis of diameter estimation methods in challenging forest
environments. Here, we validated multiple diameter estimation methods,
including two novel ones, in a new varied dataset of four different forest
sites, 11 trajectories, totaling 1458 tree observations and 1.4 hectares. We
provide recommendations for the deployment of mobile robots in a forestry
context. We conclude that our mapping method is usable in the context of
automated forest inventory, with our best method yielding a root mean square
error of 3.45 cm for our whole dataset, and 2.04 cm in ideal conditions
consisting of mature forest with well spaced trees.
