Automatic 3D Mapping for Tree Diameter Measurements in Inventory Operations

EasyChair Preprint 1281

Abstract

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.

Keyphrases: Forestry, ICP, LiDAR, Mapping

@booklet{EasyChair:1281,
  author    = {Jean-François Tremblay and Martin Béland and François Pomerleau and Richard Gagnon and Philippe Giguère},
  title     = {Automatic 3D Mapping for Tree Diameter Measurements in Inventory Operations},
  howpublished = {EasyChair Preprint 1281},
  year      = {EasyChair, 2019}}