N-QR: Natural Quick Response Codes for Multi-Robot Instance Correspondence
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| Název: | N-QR: Natural Quick Response Codes for Multi-Robot Instance Correspondence |
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| Autoři: | Glaser, Nathaniel Moore, Ravi, Rajashree, Kira, Zsolt |
| Informace o vydavateli: | 2024-03-09 |
| Druh dokumentu: | Electronic Resource |
| Abstrakt: | 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. Comment: IEEE International Conference on Robotics and Automation (ICRA), 2024 |
| Témata: | Computer Science - Robotics, text |
| URL: | |
| Dostupnost: | Open access content. Open access content |
| Other Numbers: | COO oai:arXiv.org:2403.05815 1438534339 |
| Přispívající zdroj: | CORNELL UNIV From OAIster®, provided by the OCLC Cooperative. |
| Přístupové číslo: | edsoai.on1438534339 |
| Databáze: | OAIster |
Nájsť tento článok vo Web of Science | Abstrakt: | 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.<br />Comment: IEEE International Conference on Robotics and Automation (ICRA), 2024 |
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