Agricultural robotics is an active research area due to global population growth and expectations of food and labor shortages. Robots can potentially help with tasks such as pruning, harvesting, phenotyping, and plant modeling. However, agricultural automation is hampered by the difficulty in creating high resolution 3D semantic maps in the field that would allow for safe manipulation and navigation. In this paper, we build toward solutions for this issue and showcase how the use of semantics and environmental priors can help in constructing accurate 3D maps for the target application of sorghum. Specifically, we 1) use sorghum seeds as semantic landmarks to build a visual Simultaneous Localization and Mapping (SLAM) system that enables us to map 78\\% of a sorghum range on average, compared to 38% with ORB-SLAM2; and 2) use seeds as semantic features to improve 3D reconstruction of a full sorghum panicle from images taken by a robotic in-hand camera.

翻译：农业机器人技术因全球人口增长以及对粮食和劳动力短缺的预期，成为一个活跃的研究领域。机器人有望协助完成修剪、收获、表型分析和植物建模等任务。然而，农业自动化受到在田间创建高分辨率三维语义地图的困难所阻碍，该地图是实现安全操作和导航的前提。本文针对这一问题构建了解决方案，并展示了如何利用语义信息和环境先验知识来构建针对目标应用——高粱的精确三维地图。具体而言，我们：1）利用高粱种子作为语义路标构建视觉同步定位与建图（SLAM）系统，该系统平均可映射高粱种植范围的78%，而ORB-SLAM2仅为38%；2）利用种子作为语义特征，改善从机器人手持相机拍摄的图像中重建完整高粱穗部的三维模型。