  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 and training
time required: a model is trained in minutes on a desktop computer. We show
that by exploiting the context of the environment, our method can use different
feature combinations with only limited performance variations. For example, our
approach can be used with lidar-only features, whilst still assessing complex
vegetated environments accurately, which was not demonstrated previously in the
literature in such environments. In addition, we propose an approach to assess
a traversability estimator's sensitivity to information quality and show our
method's sensitivity is low.
