The successful implementation of vision-based navigation in agricultural fields hinges upon two critical components: 1) the accurate identification of key components within the scene, and 2) the identification of lanes through the detection of boundary lines that separate the crops from the traversable ground. We propose Agronav, an end-to-end vision-based autonomous navigation framework, which outputs the centerline from the input image by sequentially processing it through semantic segmentation and semantic line detection models. We also present Agroscapes, a pixel-level annotated dataset collected across six different crops, captured from varying heights and angles. This ensures that the framework trained on Agroscapes is generalizable across both ground and aerial robotic platforms. Codes, models and dataset will be released at \href{https://github.com/shivamkumarpanda/agronav}{github.com/shivamkumarpanda/agronav}.
翻译:在农业场地中成功实现基于视觉的导航取决于两个关键组成部分:1)准确地识别场景中的关键组件,2)通过检测边界线来识别车道,以将作物与可行驶地面分离。我们提出 Agronav,一个端到端的基于视觉的自主导航框架,通过对语义分割和语义线条检测模型的序列处理,输出来自输入图像的中心线。我们还提供了 Agroscapes 数据集,这是在六种不同作物上收集的像素级注释数据集,从不同高度和角度捕获。这确保了在 Agroscapes 上训练的框架可以在地面和空中机器人平台上广泛应用。代码,模型和数据集将在 \href{https://github.com/shivamkumarpanda/agronav}{Github.com/shivamkumarpanda/agronav} 上发布。