Real-time robotic grasping, supporting a subsequent precise object-in-hand operation task, is a priority target towards highly advanced autonomous systems. However, such an algorithm which can perform sufficiently-accurate grasping with time efficiency is yet to be found. This paper proposes a novel method with a 2-stage approach that combines a fast 2D object recognition using a deep neural network and a subsequent accurate and fast 6D pose estimation based on Point Pair Feature framework to form a real-time 3D object recognition and grasping solution capable of multi-object class scenes. The proposed solution has a potential to perform robustly on real-time applications, requiring both efficiency and accuracy. In order to validate our method, we conducted extensive and thorough experiments involving laborious preparation of our own dataset. The experiment results show that the proposed method scores 97.37% accuracy in 5cm5deg metric and 99.37% in Average Distance metric. Experiment results have shown an overall 62% relative improvement (5cm5deg metric) and 52.48% (Average Distance metric) by using the proposed method. Moreover, the pose estimation execution also showed an average improvement of 47.6% in running time. Finally, to illustrate the overall efficiency of the system in real-time operations, a pick-and-place robotic experiment is conducted and has shown a convincing success rate with 90% of accuracy. This experiment video is available at https://sites.google.com/view/dl-ppf6dpose/.

实时机器人抓取支持后续的精确的在手操作任务，是高度先进的自治系统的优先目标。然而，尚未找到能够以时间效率执行足够准确的掌握的这种算法。本文提出了一种采用两阶段方法的新颖方法，该方法结合了使用深度神经网络的快速2D对象识别和基于点对特征框架的后续准确快速的6D姿势估计，从而形成了实时3D对象识别和抓取具有多对象类场景的解决方案。所提出的解决方案具有在实时应用上稳健执行的潜力，同时要求效率和准确性。为了验证我们的方法，我们进行了广泛而彻底的实验，其中涉及费力地准备自己的数据集。实验结果表明，该方法在5cm5deg度量中的准确度为97.37％，在Average Distance度量中的准确率为99.37％。实验结果表明，使用所提出的方法，总体相对改善率（5cm5deg公制）和52.48％（平均距离公制）。此外，姿势估计执行还显示出运行时间平均改善了47.6％。最后，为了说明系统在实时操作中的整体效率，进行了一个拾放机器人实验，并显示了令人信服的成功率，其准确度为90％。可以在https://sites.google.com/view/dl-ppf6dpose/上观看此实验视频。实验结果表明，使用所提出的方法，总体相对改善率（5cm5deg公制）和52.48％（平均距离公制）。此外，姿势估计执行还显示出运行时间平均改善了47.6％。最后，为了说明系统在实时操作中的整体效率，进行了一个拾放机器人实验，并显示了令人信服的成功率，其准确度为90％。可以在https://sites.google.com/view/dl-ppf6dpose/上观看此实验视频。实验结果表明，使用所提出的方法，总体相对改善率（5cm5deg公制）和52.48％（平均距离公制）。此外，姿势估计执行还显示出运行时间平均改善了47.6％。最后，为了说明系统在实时操作中的整体效率，进行了一个拾放机器人实验，并显示了令人信服的成功率，其准确度为90％。可以在https://sites.google.com/view/dl-ppf6dpose/上观看此实验视频。进行了拾放机器人实验，结果显示了令人信服的成功率，准确率高达90％。可以在https://sites.google.com/view/dl-ppf6dpose/上观看此实验视频。进行了拾放机器人实验，并显示出令人信服的成功率，准确率达到90％。可以在https://sites.google.com/view/dl-ppf6dpose/上观看此实验视频。