Learning Off-Road Terrain Traversability with Self-Supervisions Only

• URL: http://arxiv.org/abs/2305.18896v1
• Date: Tue, 30 May 2023 09:51:27 GMT
• Title: Learning Off-Road Terrain Traversability with Self-Supervisions Only
• Authors: Junwon Seo, Sungdae Sim, and Inwook Shim
• Abstract summary: Estimating the traversability of terrain should be reliable and accurate in diverse conditions for autonomous driving in off-road environments. We introduce a method for learning traversability from images that utilizes only self-supervision and no manual labels.
• Score: 2.4316550366482357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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