  Successful deployment of mobile robots in unstructured domains requires an
understanding of the environment and terrain to avoid hazardous areas, getting
stuck, and colliding with obstacles. Traversability estimation--which predicts
where in the environment a robot can travel--is one prominent approach that
tackles this problem. Existing geometric methods may ignore important semantic
considerations, while semantic segmentation approaches involve a tedious
labeling process. Recent self-supervised methods reduce labeling tedium, but
require additional data or models and tend to struggle to explicitly label
untraversable areas. To address these limitations, we introduce a
weakly-supervised method for relative traversability estimation. Our method
involves manually annotating the relative traversability of a small number of
point pairs, which significantly reduces labeling effort compared to
traditional segmentation-based methods and avoids the limitations of
self-supervised methods. We further improve the performance of our method
through a novel cross-image labeling strategy and loss function. We demonstrate
the viability and performance of our method through deployment on a mobile
robot in outdoor environments.
