MetaCropFollow: Few-Shot Adaptation with Meta-Learning for Under-Canopy Navigation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913582611955712 |
|---|---|
| author | Woehrle, Thomas Sivakumar, Arun N. Uppalapati, Naveen Chowdhary, Girish |
| author_facet | Woehrle, Thomas Sivakumar, Arun N. Uppalapati, Naveen Chowdhary, Girish |
| contents | Autonomous under-canopy navigation faces additional challenges compared to over-canopy settings - for example the tight spacing between the crop rows, degraded GPS accuracy and excessive clutter. Keypoint-based visual navigation has been shown to perform well in these conditions, however the differences between agricultural environments in terms of lighting, season, soil and crop type mean that a domain shift will likely be encountered at some point of the robot deployment. In this paper, we explore the use of Meta-Learning to overcome this domain shift using a minimal amount of data. We train a base-learner that can quickly adapt to new conditions, enabling more robust navigation in low-data regimes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_14092 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MetaCropFollow: Few-Shot Adaptation with Meta-Learning for Under-Canopy Navigation Woehrle, Thomas Sivakumar, Arun N. Uppalapati, Naveen Chowdhary, Girish Robotics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Autonomous under-canopy navigation faces additional challenges compared to over-canopy settings - for example the tight spacing between the crop rows, degraded GPS accuracy and excessive clutter. Keypoint-based visual navigation has been shown to perform well in these conditions, however the differences between agricultural environments in terms of lighting, season, soil and crop type mean that a domain shift will likely be encountered at some point of the robot deployment. In this paper, we explore the use of Meta-Learning to overcome this domain shift using a minimal amount of data. We train a base-learner that can quickly adapt to new conditions, enabling more robust navigation in low-data regimes. |
| title | MetaCropFollow: Few-Shot Adaptation with Meta-Learning for Under-Canopy Navigation |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2411.14092 |