Agricultural robots must navigate challenging dynamic and semi-structured environments. Recently, environmental modeling using LiDAR-based SLAM has shown promise in providing highly accurate geometry. However, how this chaotic environmental information can be used to achieve effective robot automation in the agricultural sector remains unexplored. In this study, we propose a novel semantic mapping and navigation framework for achieving robotic autonomy in orchards. It consists of two main components: a semantic processing module and a navigation module. First, we present a novel 3D detection network architecture, 3D-ODN, which can accurately process object instance information from point clouds. Second, we develop a framework to construct the visibility map by incorporating semantic information and terrain analysis. By combining these two critical components, our framework is evaluated in a number of key horticultural production scenarios, including a robotic system for in-situ phenotyping and daily monitoring, and a selective harvesting system in apple orchards. The experimental results show that our method can ensure high accuracy in understanding the environment and enable reliable robot autonomy in agricultural environments.

翻译：农业机器人必须在具有挑战性的动态半结构化环境中导航。近年来，基于激光雷达SLAM的环境建模在提供高精度几何信息方面展现出潜力。然而，如何利用这些混沌环境信息实现农业领域的有效机器人自动化仍待探索。本研究提出一种新型语义建图与导航框架，用于实现果园场景中的机器人自主性。该框架包含两大核心模块：语义处理模块与导航模块。首先，我们提出一种新颖的三维检测网络架构3D-ODN，能够从点云数据中精确处理物体实例信息。其次，我们通过融合语义信息与地形分析，构建了可见性地图生成框架。通过整合这两个关键组件，本研究在多个关键园艺生产场景中对所提框架进行了评估，包括用于原位表型分析与日常监测的机器人系统，以及苹果园的精准采摘系统。实验结果表明，该方法能确保环境理解的高准确度，并在农业环境中实现可靠的机器人自主性。