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In this research, a fully neural network based visual perception frameworkfor autonomous apple harvesting is proposed. The proposed framework includes amulti-function neural network for fruit recognition and a Pointnet graspestimation to determine the proper grasp pose to guide the robotic execution.Fruit recognition takes raw input of RGB images from the RGB-D camera toperform fruit detection and instance segmentation, and Pointnet graspestimation take point cloud of each fruit as input and output the prediction ofgrasp pose for each of fruits. The proposed framework is validated by usingRGB-D images collected from laboratory and orchard environments, a roboticgrasping test in a controlled environment is also included in the experiments.Experimental shows that the proposed framework can accurately localise andestimate the grasp pose for robotic grasping.

Real-Time Fruit Recognition and Grasping Estimation for Autonomous Apple Harvesting