  Traversability estimation for mobile robots in off-road environments requires
more than conventional semantic segmentation used in constrained environments
like on-road conditions. Recently, approaches to learning a traversability
estimation from past driving experiences in a self-supervised manner are
arising as they can significantly reduce human labeling costs and labeling
errors. However, the self-supervised data only provide supervision for the
actually traversed regions, inducing epistemic uncertainty according to the
scarcity of negative information. Negative data are rarely harvested as the
system can be severely damaged while logging the data. To mitigate the
uncertainty, we introduce a deep metric learning-based method to incorporate
unlabeled data with a few positive and negative prototypes in order to leverage
the uncertainty, which jointly learns using semantic segmentation and
traversability regression. To firmly evaluate the proposed framework, we
introduce a new evaluation metric that comprehensively evaluates the
segmentation and regression. Additionally, we construct a driving dataset
`Dtrail' in off-road environments with a mobile robot platform, which is
composed of a wide variety of negative data. We examine our method on Dtrail as
well as the publicly available SemanticKITTI dataset.
