Usage of purely vision based solutions for row switching is not well explored
in existing vision based crop row navigation frameworks. This method only uses
RGB images for local feature matching based visual feedback to exit crop row.
Depth images were used at crop row end to estimate the navigation distance
within headland. The algorithm was tested on diverse headland areas with soil
and vegetation. The proposed method could reach the end of the crop row and
then navigate into the headland completely leaving behind the crop row with an
error margin of 50 cm.

本文中介绍了一种基于视觉反馈的纯视觉方案，使用 RGB 图像进行局部特征匹配来退出作物行，并在作物行末端使用深度图像来估算导航距离，模拟在多样化的围栏区域中进行的算法测试表明其误差在 50cm 以内。

视觉化基于农业机器人导航的农田作物行出口

Leaving the Lines Behind: Vision-Based Crop Row Exit for Agricultural  Robot Navigation

Autonomous driving techniques have been flourishing in recent years while
thirsting for huge amounts of high-quality data. However, it is difficult for
real-world datasets to keep up with the pace of changing requirements due to
their expensive and time-consuming experimental and labeling costs. Therefore,
more and more researchers are turning to synthetic datasets to easily generate
rich and changeable data as an effective complement to the real world and to
improve the performance of algorithms. In this paper, we summarize the
evolution of synthetic dataset generation methods and review the work to date
in synthetic datasets related to single and multi-task categories for to
autonomous driving study. We also discuss the role that synthetic dataset plays
the evaluation, gap test, and positive effect in autonomous driving related
algorithm testing, especially on trustworthiness and safety aspects. Finally,
we discuss general trends and possible development directions. To the best of
our knowledge, this is the first survey focusing on the application of
synthetic datasets in autonomous driving. This survey also raises awareness of
the problems of real-world deployment of autonomous driving technology and
provides researchers with a possible solution.

本研究综述了合成数据集生成方法的演变，并回顾了应用于自动驾驶研究的单任务和多任务合成数据集的相关工作，讨论了其在自动驾驶相关算法测试中的作用，尤其是在可信性和安全性方面。此外，该综述还讨论了合成数据集在自动驾驶技术实际部署方面的问题，并提供了一种可能的解决方案。