In this work, we propose a novel framework for achieving robotic autonomy in orchards. It consists of two key steps: perception and semantic mapping. In the perception step, we introduce a 3D detection method that accurately identifies objects directly on point cloud maps. In the semantic mapping step, we develop a mapping module that constructs a visibility graph map by incorporating object-level information and terrain analysis. By combining these two steps, our framework improves the autonomy of agricultural robots in orchard environments. The accurate detection of objects and the construction of a semantic map enable the robot to navigate autonomously, perform tasks such as fruit harvesting, and acquire actionable information for efficient agricultural production.

翻译：本文提出了一种实现果园环境下机器人自主性的新型框架。该框架包含两个关键步骤：感知与语义建图。在感知步骤中，我们引入了一种可直接在点云地图上精确识别目标的3D检测方法。在语义建图步骤中，我们开发了一个通过融合目标级信息与地形分析来构建可见性图（visibility graph）的建图模块。通过整合这两个步骤，我们的框架提升了农业机器人在果园环境中的自主性。精确的目标检测与语义地图的构建使机器人能够自主导航、执行如水果采摘等任务，并获取用于高效农业生产的可操作信息。