Agricultural robots are serving as powerful assistants across a wide range of agricultural tasks, nevertheless, still heavily relying on manual operations or railway systems for movement. The AgriVLN method and the A2A benchmark pioneeringly extended Vision-and-Language Navigation (VLN) to the agricultural domain, enabling a robot to navigate to a target position following a natural language instruction. Unlike human binocular vision, most agricultural robots are only given a single camera for monocular vision, which results in limited spatial perception. To bridge this gap, we present the method of Agricultural Vision-and-Language Navigation with Monocular Depth Estimation (MDE-AgriVLN), in which we propose the MDE module generating depth features from RGB images, to assist the decision-maker on multimodal reasoning. When evaluated on the A2A benchmark, our MDE-AgriVLN method successfully increases Success Rate from 0.23 to 0.32 and decreases Navigation Error from 4.43m to 4.08m, demonstrating the state-of-the-art performance in the agricultural VLN domain. Code: https://github.com/AlexTraveling/MDE-AgriVLN.

翻译：农业机器人正作为强大的助手服务于广泛的农业任务，然而其移动仍严重依赖人工操作或轨道系统。AgriVLN方法与A2A基准率先将视觉与语言导航（VLN）扩展至农业领域，使机器人能够依据自然语言指令导航至目标位置。与人类的双目视觉不同，大多数农业机器人仅配备单目视觉的单摄像头，导致空间感知能力受限。为弥补这一差距，我们提出了基于单目深度估计的农业视觉与语言导航方法（MDE-AgriVLN），其中我们设计了可从RGB图像生成深度特征的MDE模块，以辅助决策器进行多模态推理。在A2A基准上的评估结果表明，我们的MDE-AgriVLN方法将成功率从0.23提升至0.32，并将导航误差从4.43米降低至4.08米，展现了在农业VLN领域最先进的性能。代码：https://github.com/AlexTraveling/MDE-AgriVLN。