農業用視覚言語ナビゲーションのための走行可能性推定アラーム
TEA-AgriVLN: Traversability Estimation Alarm for Agricultural Vision-and-Language Navigation
農業シーンでのVLN-CEにおいて、カメラ画像の走行可能性を推定し、予測行動と矛盾する場合に警報を出すTEAモジュールを提案し、AgriVLNに統合して性能を向上させた。
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著者: Xiaobei Zhao, Xingqi Lyu, Xin Chen, Xiang Li
分類: cs.RO
原文アブストラクト
Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires an agent to follow a natural language instruction, predicting a sequence of low-level actions to navigate a robot from a starting point to a target location. The A2A benchmark and the AgriVLN method pioneeringly extended VLN-CE from indoor scenes to agricultural scenes, while we observed a challenging distinction: In indoor scenes, whether a zone is traversable tends to be clear to classify, such as wood floors are traversable but concrete walls are not. In agricultural scenes, however, this issue tends to be ambiguous, such as an unripe cornfield might be traversable for a robotic dog but might be non-traversable for a human. To address this issue, we propose the TEA module, which estimates the traversability of the camera image, then alarm the decision-maker for rethinking when the predicted action does not align with the traversability map. We integrate it into the AgriVLN backbone to build our TEA-AgriVLN method. When evaluated on A2A, it improves Success Rate (SR) from 0.47 to 0.54 and Navigation Error (NE) from 2.91 m to 2.70 m, showing the state-of-the-art performance in the agricultural VLN-CE domain. We further implement the ablation studies and the case study, discussing the effectiveness and limitations of TEA on different ground categories and scene classes. Code: https://github.com/AlexTraveling/TEA-AgriVLN.
