W-RIZZ: A Weakly-Supervised Framework for Relative Traversability Estimation in Mobile Robotics
<|reference_start|>W-RIZZ: A Weakly-Supervised Framework for Relative Traversability Estimation in Mobile Robotics: 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.<|reference_end|>
@article{schreiber2024w-rizz:,
    title={W-RIZZ: A Weakly-Supervised Framework for Relative Traversability
  Estimation in Mobile Robotics},
    author={Andre Schreiber, Arun N. Sivakumar, Peter Du, Mateus V. Gasparino,
  Girish Chowdhary, Katherine Driggs-Campbell},
    journal={arXiv preprint arXiv:2406.02822},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02822},
    primaryClass={cs.RO}
}