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