论文标题
实时的水果识别和掌握自动苹果收获的估计
Real-Time Fruit Recognition and Grasping Estimation for Autonomous Apple Harvesting
论文作者
论文摘要
在这项研究中，提出了一个完全基于神经网络的视觉感知框架，用于自主苹果收获。所提出的框架包括用于果实识别的多功能神经网络和点网的估计，以确定适当的掌握姿势以指导机器人执行。水果识别从RGB-D摄像机中对RGB图像进行原始输入，以执行水果检测和实例分割，而PointNet Grasp估计将每个水果的点云作为输入，并输出每个水果的Grasp姿势的预测。通过使用从实验室和果园环境中收集的RGB-D图像来验证所提出的框架，在实验中还包括在受控环境中进行的机器人握把测试。实验表明，所提出的框架可以准确地定位并估计抓握姿势以进行机器人抓握。
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.
