AdaCropFollow: 視覚による樹冠下ナビゲーションのための自己教師ありオンライン適応
AdaCropFollow: Self-Supervised Online Adaptation for Visual Under-Canopy Navigation
農業ロボットの樹冠下ナビゲーションにおいて、視覚基盤モデルと幾何学的事前知識、擬似ラベルを用いた自己教師ありオンライン適応により、展開時のドメインシフトに適応する手法を提案した。
著者: Arun N. Sivakumar, Federico Magistri, Mateus V. Gasparino, Jens Behley, Cyrill Stachniss, Girish Chowdhary
分類: cs.RO, cs.CV
原文アブストラクト
Under-canopy agricultural robots can enable various applications like precise monitoring, spraying, weeding, and plant manipulation tasks throughout the growing season. Autonomous navigation under the canopy is challenging due to the degradation in accuracy of RTK-GPS and the large variability in the visual appearance of the scene over time. In prior work, we developed a supervised learning-based perception system with semantic keypoint representation and deployed this in various field conditions. A large number of failures of this system can be attributed to the inability of the perception model to adapt to the domain shift encountered during deployment. In this paper, we propose a self-supervised online adaptation method for adapting the semantic keypoint representation using a visual foundational model, geometric prior, and pseudo labeling. Our preliminary experiments show that with minimal data and fine-tuning of parameters, the keypoint prediction model trained with labels on the source domain can be adapted in a self-supervised manner to various challenging target domains onboard the robot computer using our method. This can enable fully autonomous row-following capability in under-canopy robots across fields and crops without requiring human intervention.
