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 \acfp{SCNN} to assess \ac{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.

翻译：在非结构化植被环境中的自主导航仍然是一项开放性挑战。为在此类环境中成功运行，地面车辆必须评估环境的可通行性，并判断哪些植被具有足够柔韧性以便穿越。本文提出了一种新颖方法，该方法结合了高保真度、高特征密度的三维体素表征，同时利用结构化上下文与稀疏卷积神经网络（SCNN）的稀疏特性，以评估密集植被环境中的地形可通行性（TE）。所提方法在向学术界公开的、带有精确标注的真实世界数据集上进行了全面评估。实验表明，该方法在具有挑战性的场景中显著优于现有技术（在0.1米体素分辨率下，MCC分数为0.59对0.39），并能泛化至未见环境。此外，该方法在训练数据量和训练时间上均具有经济性：在台式计算机上数分钟内即可完成模型训练。研究表明，通过利用环境上下文信息，本方法可在仅产生有限性能波动的情况下采用不同特征组合。例如，本方法可使用仅基于激光雷达的特征，仍能准确评估复杂植被环境——此类场景在现有文献中尚未得到类似验证。进一步地，我们提出了一种评估可通行性估计器对信息质量敏感性的方法，并证明本方法的该敏感性较低。