Dex-Net AR: Distributed Deep Grasp Planning Using a Commodity Cellphone and Augmented Reality App-猫眼课题宝

DOI：

10.1109/icra40945.2020.9197247

发表时间：

2020-05

期刊：

2020 IEEE International Conference on Robotics and Automation (ICRA)

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通讯作者：

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其他

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作者：

Harry Zhang;Jeffrey Ichnowski;Yahav Avigal;Joseph E. Gonzales;I. Stoica;Ken Goldberg

消费者对移动的手机应用中增强现实（AR）的需求，例如Apple ARKit。这样的应用程序有可能扩大访问机器人抓取规划系统，如Dex-Net。AR应用程序使用运动结构方法从相机在物体周围移动时拍摄的RGB图像序列中计算点云。然而，由于估计误差，所得到的点云通常是有噪声的。我们提出了一个分布式管道Dex-Net AR，它允许将点云上传到我们实验室的服务器，由Dex-Net抓取规划器进行清理和评估，以生成抓取轴，该轴将返回并显示为对象上的叠加层。我们使用iPhone和ARKit实现了Dex-Net AR，并将结果与高性能深度传感器生成的结果进行了比较。AR在较硬的对抗性物体上的成功率高于传统的深度图像。服务器URL为https://sites.google.com/berkeley.edu/dex-net-ar/home

Consumer demand for augmented reality (AR) in mobile phone applications, such as the Apple ARKit. Such applications have potential to expand access to robot grasp planning systems such as Dex-Net. AR apps use structure from motion methods to compute a point cloud from a sequence of RGB images taken by the camera as it is moved around an object. However, the resulting point clouds are often noisy due to estimation errors. We present a distributed pipeline, Dex-Net AR, that allows point clouds to be uploaded to a server in our lab, cleaned, and evaluated by Dex-Net grasp planner to generate a grasp axis that is returned and displayed as an overlay on the object. We implement Dex-Net AR using the iPhone and ARKit and compare results with those generated with high-performance depth sensors. The success rates with AR on harder adversarial objects are higher than traditional depth images. The server URL is https://sites.google.com/berkeley.edu/dex-net-ar/home

Dex-Net AR: Distributed Deep Grasp Planning Using a Commodity Cellphone and Augmented Reality App

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