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Agricultural robots must navigate challenging dynamic and semi-structuredenvironments. Recently, environmental modeling using LiDAR-based SLAM has shownpromise in providing highly accurate geometry. However, how this chaoticenvironmental information can be used to achieve effective robot automation inthe agricultural sector remains unexplored. In this study, we propose a novelsemantic mapping and navigation framework for achieving robotic autonomy inorchards. It consists of two main components: a semantic processing module anda navigation module. First, we present a novel 3D detection networkarchitecture, 3D-ODN, which can accurately process object instance informationfrom point clouds. Second, we develop a framework to construct the visibilitymap by incorporating semantic information and terrain analysis. By combiningthese two critical components, our framework is evaluated in a number of keyhorticultural production scenarios, including a robotic system for in-situphenotyping and daily monitoring, and a selective harvesting system in appleorchards. The experimental results show that our method can ensure highaccuracy in understanding the environment and enable reliable robot autonomy inagricultural environments.

A Novel Perception and Semantic Mapping Method for Robot Autonomy in Orchards