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Learning Off-Road Terrain Traversability with Self-Supervisions Only
- Source :
- IEEE Robotics and Automation Letters, 8.8 (2023):4617-4624
- Publication Year :
- 2023
-
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.<br />Comment: Accepted to IEEE Robotics and Automation Letters. Our video can be found at https://bit.ly/3YdKanw
Details
- Database :
- arXiv
- Journal :
- IEEE Robotics and Automation Letters, 8.8 (2023):4617-4624
- Publication Type :
- Report
- Accession number :
- edsarx.2305.18896
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/LRA.2023.3284356