Learning Off-Road Terrain Traversability with Self-Supervisions Only
Junwon Seo Sungdae Sim Inwook Shim
Estimating the traversability of terrain should be reliable and accurate in
diverse conditions for autonomous driving in off-road environments. However,
learning-based approaches often yield unreliable results when confronted with
unfamiliar contexts, and it is challenging to obtain manual annotations
frequently for new circumstances. In this paper, we introduce a method for
learning traversability from images that utilizes only self-supervision and no
manual labels, enabling it to easily learn traversability in new circumstances.
To this end, we first generate self-supervised traversability labels from past
driving trajectories by labeling regions traversed by the vehicle as highly
traversable. Using the self-supervised labels, we then train a neural network
that identifies terrains that are safe to traverse from an image using a
one-class classification algorithm. Additionally, we supplement the limitations
of self-supervised labels by incorporating methods of self-supervised learning
of visual representations. To conduct a comprehensive evaluation, we collect
data in a variety of driving environments and perceptual conditions and show
that our method produces reliable estimations in various environments. In
addition, the experimental results validate that our method outperforms other
self-supervised traversability estimation methods and achieves comparable
performances with supervised learning methods trained on manually labeled data.