[ros-users] [Discourse.ros.org] [Next Generation ROS] ROS2 Grasp Library (MoveIt + OpenVINO) - Initial Release 0.4.0

[Sharron LIU via Discourse.ros.org]

Hi All,

Were happy to announce the initial release of ROS2 Grasp Library, with OpenVINO enabling and MoveIt compliance.



ROS2 Grasp Library ([https://github.com/intel/ros2_grasp_library](https://github.com/intel/ros2_grasp_library)) enables state-of-the-art CNN based deep learning grasp detection algorithms on ROS2 for visual based industrial robot manipulation. This package provide ROS2 interfaces compliant with the [MoveIt](http://moveit.ros.org/) motion planning framework which is supported by most of the [robot models](https://moveit.ros.org/robots) in ROS industrial. This package delivers



* A ROS2 Grasp Planner providing grasp planning service, as an extensible capability of MoveIt ([moveit_msgs::srv::GraspPlanning](http://docs.ros.org/api/moveit_msgs/html/srv/GraspPlanning.html))

* A ROS2 Grasp Detector generic interface, collaborating with Grasp Planner for grasp detection. Also a specific back-end algorithm enabled under this interface: [Grasp Pose Detection](https://github.com/atenpas/gpd) with Intel [OpenVINO](https://software.intel.com/en-us/openvino-toolkit) toolkit[1]

* Grasp transformation from camera frame to a specified target frame expected in the visual manipulation; Grasp translation to the MoveIt Interfaces ([moveit_msgs::msg::Grasp](http://docs.ros.org/api/moveit_msgs/html/msg/Grasp.html))

* A 'service-driven' grasp detection mechanism (via configure [auto_mode](https://github.com/sharronliu/ros2_grasp_library/blob/master/docs/tutorials_3_grasp_library_launch_options.md)) to optimize CPU load for real-time processing

![ros2_grasp_library|690x466,75%](upload://hk77uBzErO9SqSQV2zxqm0hA3zY.jpeg) 



The package was verified with Ubuntu 18.04 Bionic and ROS2 Crystal release.

Verification with ROS2 **MoveIt 2.0+** is still working in progress. Before this, we have verified the grasp detection with **MoveIt 1.0** Melodic branch (tag 0.10.8) and our visual pick & place application.



Thanks,

Sharron



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[1] Brief introduction to Intel DLDT toolkit and Intel OpenVINO toolkit



Intel [DLDT](https://github.com/opencv/dldt) is a Deep Learning Deployment Toolkit common to various architectures. The toolkit allows developers to convert pre-trained deep learning models into optimized Intermediate Representation (IR) models, then deploy the IR models through a high-level C++ Inference Engine API integrated with application logic. Additionally, [Open Model Zoo](https://github.com/opencv/open_model_zoo) provides more than 100 pre-trained optimized deep learning models and a set of demos to expedite development of high-performance deep learning inference applications. Online tutorials are availble for

* [Inference Engine Build Instructions](https://github.com/opencv/dldt/blob/2018/inference-engine/README.md)



Intel [OpenVINO](https://software.intel.com/en-us/openvino-toolkit) (Open Visual Inference & Neural Network Optimization) toolkit enables CNN-based deep learning inference at the edge computation, extends workloads across Intel hardware (including accelerators) and maximizes performance. The toolkit supports heterogeneous execution across various compution vision devices -- CPU, GPU, Intel Movidius NCS, and FPGA -- using a common API. Online tutorials are available for

* [Model Optimize Developer Guide](https://software.intel.com/en-us/articles/OpenVINO-ModelOptimizer)

* [Inference Engine Developer Guide](https://software.intel.com/en-us/articles/OpenVINO-InferEngine)

* [Intel Neural Compute Stick 2](https://software.intel.com/en-us/neural-compute-stick/get-started)











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