Original scientific paper
https://doi.org/10.5552/crojfe.2024.2229
Assessing Forest Traversability for Autonomous Mobile Systems Using Close-Range Airborne Laser Scanning
Tuomas Yrttimaa
; School of Forest Sciences University of Eastern Finland 80101 Joensuu FINLAND
Shigemichi Matsuzaki
; Department of Computer Science and Engineering Toyohashi University of Technology Toyohashi JAPAN
Ville Kankare
; School of Forest Sciences University of Eastern Finland 80101 Joensuu FINLAND
Samuli Junttila
; School of Forest Sciences University of Eastern Finland 80101 Joensuu FINLAND
Ninni Saarinen
; School of Forest Sciences University of Eastern Finland 80101 Joensuu FINLAND
Antero Kukko
; Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute National Land Survey of Finland 02430 Masala FINLAND
Juha Hyyppä
; Department of Photogrammetry and Remote Sensing Finnish Geospatial Research Institute National Land Survey of Finland 02430 Masala FINLAND
Jun Miura
; Department of Computer Science and Engineering Toyohashi University of Technology Toyohashi JAPAN
Mikko Vastaranta
; School of Forest Sciences University of Eastern Finland 80101 Joensuu FINLAND
Abstract
Advances in sensor technology and computing performance has brought us into an era of digital forestry where a forest environment can be digitally replicated. At the same time, an increasing interest in the use of unmanned vehicles and other autonomous mobile systems (AMSs) in forest mapping and operations has emerged. However, a forest is an unstructured and rather complex environment for AMSs to operate in, and usually some kind of a priori information of traversability is required. The aim of this study was to assess forest traversability for AMSs using high-density airborne laser scanning (ALS) point clouds. It was assumed that such point clouds acquired from a helicopter flying at a low altitude can be used to characterise vegetation obstacles affecting forest traversability. A voxel-based vegetation occupancy analysis was carried out with the aim to detect open space to define traversable three-dimensional space. The experimental setup included seven sample plots (32×32 m) representing diverse boreal forest structures. Terrestrial laser scanning (TLS) was used for obtaining reference for vegetation occupancy. Comparison between ALS and TLS revealed an overall accuracy of 0.85–0.94 with a recall of 0.78–0.91 and a precision of 0.62–0.74 for ALS-based voxel classification for vegetation occupancy depending on forest structure. This implies that up to 91% of voxels assigned a classification »occupied« based on the TLS could be correctly classified using the ALS, while up to 74% of voxels assigned a classification »occupied« using the ALS were occupied based on the TLS. Density of low vegetation accounted for 83% of the variation in accuracy and precision. The feasibility of vegetation occupancy information to be used by an AMS for navigation was also demonstrated. It was assumed that the ALS data convey as sufficient information of AMS path planning as does the TLS data. The experiments showed that out of 1393 randomly generated paths based on empty space detected by the ALS, 72% were considered feasible when validated with the TLS data. The success rate in path planning varied from 0.54 to 0.92 between the sample plots and was seemingly affected by vegetation density that accounted for 53% of variation in success rate. Altogether, the demonstrated possibility to predefine forest traversability using remote sensing will support the use of AMSs in forestry.
Keywords
Hrčak ID:
311936
URI
Publication date:
12.1.2024.
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