ForestTrav: Accurate, Efficient and Deployable Forest Traversability Estimation for Autonomous Ground Vehicles
Fabio Ruetz, Nicholas Lawrance, Emili Hern\'andez, Paulo Borges,, Thierry Peynot

TL;DR
This paper introduces ForestTrav, a novel, efficient method for assessing ground vehicle traversability in vegetated environments using a 3D voxel representation and sparse convolutional networks, outperforming existing methods.
Contribution
The paper presents a new high-fidelity 3D voxel-based traversability estimation method that is accurate, efficient, and capable of generalizing to unseen environments with minimal training data.
Findings
Outperforms state-of-the-art MCC score of 0.39 with 0.59
Generalizes well to unseen environments
Requires only minutes of training on a desktop
Abstract
Autonomous navigation in unstructured vegetated environments remains an open challenge. To successfully operate in these settings, ground vehicles must assess the traversability of the environment and determine which vegetation is pliable enough to push through. In this work, we propose a novel method that combines a high-fidelity and feature-rich 3D voxel representation while leveraging the structural context and sparseness of SCNN's to assess Traversability Estimation (TE) in densely vegetated environments. The proposed method is thoroughly evaluated on an accurately-labeled real-world data set that we provide to the community. It is shown to outperform state-of-the-art methods by a significant margin (0.59 vs. 0.39 MCC score at 0.1m voxel resolution) in challenging scenes and to generalize to unseen environments. In addition, the method is economical in the amount of training data…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Remote Sensing and LiDAR Applications · Wildlife-Road Interactions and Conservation
