SOURCE-LINKED INTELLIGENCE
Learning to Drive on Mars: Visual Multimodal Traversability Estimation for Off-World Navigation
Autonomous navigation on Mars requires vehicles to distinguish between traversable terrains across diverse and visually challenging environments. However, progress in learning-based navigation for off-world environments has been limited by the lack of large-scale datasets. Since landing in Jezero Crater, the Mars 2020 Perseverance rover has traversed terrain ranging from sandy dunes, rocky patches, and flat bedrocks. As a result, this paper presents a dataset spanning 500 sols and 45km of trajectories driven by both human operators and the onboard planner, ENav. Our dataset contains grayscale
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-21T17:45:18.000Z
First collected: 2026-09-23T06:11:12.848Z. This is not the publication date.