Automatic 3D Mapping for Tree Diameter Measurements in Inventory Operations

TL;DR

This paper presents a novel 3D mapping approach using iterative closest point algorithms to automatically measure tree diameters in forests, validated across diverse environments with high accuracy, aiding automated forest inventory.

Contribution

It introduces and validates new diameter estimation methods, including two novel ones, for mobile robot-based forest inventory in challenging environments.

Findings

Root mean square error of 3.45 cm overall

Error reduced to 2.04 cm in ideal conditions

Validated methods across four forest sites

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.