Online Tree Reconstruction and Forest Inventory on a Mobile Robotic System

TL;DR

This paper introduces a real-time, mobile laser scanning system for forest inventory that efficiently reconstructs trees and updates data incrementally, achieving high accuracy comparable to traditional TLS methods without post-processing.

Contribution

The work presents a novel online forest inventory system using mobile laser scanners with incremental data updating and robust tree modeling, reducing processing time and effort.

Findings

Achieves RMSE of 1.93 cm in tree measurements

Operates effectively across different forest types

Eliminates post-processing after data collection

Abstract

Terrestrial laser scanning (TLS) is the standard technique used to create accurate point clouds for digital forest inventories. However, the measurement process is demanding, requiring up to two days per hectare for data collection, significant data storage, as well as resource-heavy post-processing of 3D data. In this work, we present a real-time mapping and analysis system that enables online generation of forest inventories using mobile laser scanners that can be mounted e.g. on mobile robots. Given incrementally created and locally accurate submaps-data payloads-our approach extracts tree candidates using a custom, Voronoi-inspired clustering algorithm. Tree candidates are reconstructed using an adapted Hough algorithm, which enables robust modeling of the tree stem. Further, we explicitly incorporate the incremental nature of the data collection by consistently updating the database using a pose graph LiDAR SLAM system. This enables us to refine our estimates of the tree traits if an area is revisited later during a mission. We demonstrate competitive accuracy to TLS or manual measurements using laser scanners that we mounted on backpacks or mobile robots operating in conifer, broad-leaf and mixed forests. Our results achieve RMSE of 1.93 cm, a bias of 0.65 cm and a standard deviation of 1.81 cm (averaged across these sequences)-with no post-processing required after the mission is complete.