N-QR: Natural Quick Response Codes for Multi-Robot Instance Correspondence
<|reference_start|>N-QR: Natural Quick Response Codes for Multi-Robot Instance Correspondence: Image correspondence serves as the backbone for many tasks in robotics, such as visual fusion, localization, and mapping. However, existing correspondence methods do not scale to large multi-robot systems, and they struggle when image features are weak, ambiguous, or evolving. In response, we propose Natural Quick Response codes, or N-QR, which enables rapid and reliable correspondence between large-scale teams of heterogeneous robots. Our method works like a QR code, using keypoint-based alignment, rapid encoding, and error correction via ensembles of image patches of natural patterns. We deploy our algorithm in a production-scale robotic farm, where groups of growing plants must be matched across many robots. We demonstrate superior performance compared to several baselines, obtaining a retrieval accuracy of 88.2%. Our method generalizes to a farm with 100 robots, achieving a 12.5x reduction in bandwidth and a 20.5x speedup. We leverage our method to correspond 700k plants and confirm a link between a robotic seeding policy and germination.<|reference_end|>
@article{glaser2024n-qr:,
    title={N-QR: Natural Quick Response Codes for Multi-Robot Instance
  Correspondence},
    author={Nathaniel Moore Glaser, Rajashree Ravi, Zsolt Kira},
    journal={arXiv preprint arXiv:2403.05815},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05815},
    primaryClass={cs.RO}
}