Self-Supervised 3D Traversability Estimation With Proxy Bank Guidance

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, resulting in epistemic uncertainty due to the lack of knowledge on non-traversable regions, also referred to as negative data. Negative data can rarely be collected as the system can be severely damaged while logging the data. To mitigate the uncertainty in the estimation, we introduce a deep metric learning-based method to incorporate unlabeled data with a few positive and negative prototypes. Our method jointly learns binary segmentation that reduces uncertainty in addition to the regression of traversability. 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 numerous complex and diverse representations of off-road environments. We examine our method on Dtrail as well as the publicly available SemanticKITTI dataset.