MetaCropFollow: Few-Shot Adaptation with Meta-Learning for Under-Canopy Navigation

• URL: http://arxiv.org/abs/2411.14092v1
• Date: Thu, 21 Nov 2024 12:58:09 GMT
• Title: MetaCropFollow: Few-Shot Adaptation with Meta-Learning for Under-Canopy Navigation
• Authors: Thomas Woehrle, Arun N. Sivakumar, Naveen Uppalapati, Girish Chowdhary,
• Abstract summary: Keypoint-based visual navigation has been shown to perform well in under-canopy environments. We train a base-learner that can quickly adapt to new conditions, enabling more robust navigation in low-data regimes.
• Score: 4.923031976899536
• License:
• Abstract: 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.

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