MetaCropFollow: Few-Shot Adaptation with Meta-Learning for Under-Canopy Navigation
This addresses domain adaptation for agricultural robots, but it is incremental as it applies existing meta-learning techniques to a specific navigation challenge.
The paper tackled the problem of domain shift in autonomous under-canopy navigation by using meta-learning to adapt to new agricultural environments with minimal data, achieving robust navigation in low-data regimes.
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.