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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.05815 |
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| _version_ | 1866909133491404800 |
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| author | Glaser, Nathaniel Moore Ravi, Rajashree Kira, Zsolt |
| author_facet | Glaser, Nathaniel Moore Ravi, Rajashree Kira, Zsolt |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05815 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | N-QR: Natural Quick Response Codes for Multi-Robot Instance Correspondence Glaser, Nathaniel Moore Ravi, Rajashree Kira, Zsolt Robotics 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. |
| title | N-QR: Natural Quick Response Codes for Multi-Robot Instance Correspondence |
| topic | Robotics |
| url | https://arxiv.org/abs/2403.05815 |