Deep Object Pose Estimation - ROS Inference

This is the official DOPE ROS package for detection and 6-DoF pose estimation of known objects from an RGB camera. The network has been trained on the following YCB objects: cracker box, sugar box, tomato soup can, mustard bottle, potted meat can, and gelatin box. For more details, see our CoRL 2018 paper and video.

Note: The instructions below refer to inference only. Training code is also provided but not supported. Thank you to @Blaine141 You can check out how to train DOPE on a single GPU and using NVISII.

Updates

2021/12/13 - Added a NViSII script to generate synthetic data for training DOPE. See this readme for more details. We also added the update training and inference (without ROS) scripts for the NViSII paper here.

2021/10/20 - Added ROS2 Foxy inference support through Isaac ROS DOPE package for Jetson and x86+CUDA-capable GPU.

2021/08/07 - Added publishing belief maps. Thank you to Martin Günther.

2020/03/09 - Added HOPE weights to google drive, the 3d models, and the objects dimensions to config. Tremblay et al., IROS 2020. The HOPE dataset can be found here and is also part of the BOP challenge

2020/02/09 - Upgraded DOPE to use Python 3. Updated Dockerfile to use Python3-compatible ROS Noetic. The Python 2.7/ROS Kinetic is still available on the 'ros-kinetic' branch.

2020/16/03 - Added a wiki (thanks to @saratrajput)

Installing

We have tested on Ubuntu 20.04 with ROS Noetic with an NVIDIA Titan X and RTX 2080ti with Python 3.8. The code may work on other systems.

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NOTE

For hardware-accelerated ROS2 inference support, please visit Isaac ROS DOPE which has been tested with ROS2 Foxy on Jetson AGX Xavier/JetPack 4.6 and on x86/Ubuntu 20.04 with RTX3060i.

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The following steps describe the native installation. Alternatively, use the provided Docker image and skip to Step #7.

1. Install ROS

   Follow these instructions. You can select any of the default configurations in step 1.4; even the ROS-Base (Bare Bones) package (ros-noetic-ros-base) is enough.

2. Create a catkin workspace (if you do not already have one). To create a catkin workspace, follow these instructions:

   $ mkdir -p ~/catkin_ws/src   # Replace `catkin_ws` with the name of your workspace
   $ cd ~/catkin_ws/
   $ catkin_make
   

3. Download the DOPE code

   $ cd ~/catkin_ws/src
   $ git clone https://github.com/NVlabs/Deep_Object_Pose.git dope
   

4. Install PyTorch (you might need a different installation based on your configuration, find details here)

   pip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
   

5. Install python dependencies

   $ cd ~/catkin_ws/src/dope
   $ python3 -m pip install -r requirements.txt
   

6. Install camera_info_manager_py dependency

   $ cd /tmp
   $ git clone https://github.com/ros-perception/camera_info_manager_py
   $ cp -r camera_info_manager_py/src/camera_info_manager /opt/ros/noetic/lib/python3/dist-packages/
   

7. Install ROS dependencies

   $ cd ~/catkin_ws
   $ rosdep install --from-paths src -i --rosdistro noetic
   $ sudo apt-get install ros-noetic-rosbash ros-noetic-ros-comm
   

8. Build

   $ cd ~/catkin_ws
   $ catkin_make
   

9. Download the weights and save them to the weights folder, i.e., ~/catkin_ws/src/dope/weights/.

Running

1. Start ROS master

   $ cd ~/catkin_ws
   $ source devel/setup.bash
   $ roscore
   

2. Start camera node (or start your own camera node)

   $ roslaunch dope camera.launch  # Publishes RGB images to `/dope/webcam_rgb_raw`
   

   The camera must publish a correct camera_info topic to enable DOPE to compute the correct poses. Basically all ROS drivers have a camera_info_url parameter where you can set the calibration info (but most ROS drivers include a reasonable default).

   For details on calibration and rectification of your camera see the camera tutorial.

3. Edit config info (if desired) in ~/catkin_ws/src/dope/config/config_pose.yaml

   • topic_camera: RGB topic to listen to
   • topic_camera_info: camera info topic to listen to
   • topic_publishing: topic namespace for publishing
   • input_is_rectified: Whether the input images are rectified. It is strongly suggested to use a rectified input topic.
   • downscale_height: If the input image is larger than this, scale it down to this pixel height. Very large input images eat up all the GPU memory and slow down inference. Also, DOPE works best when the object size (in pixels) has appeared in the training data (which is downscaled to 400 px). For these reasons, downscaling large input images to something reasonable (e.g., 400-500 px) improves memory consumption, inference speed and recognition results.
   • weights: dictionary of object names and there weights path name, comment out any line to disable detection/estimation of that object
   • dimensions: dictionary of dimensions for the objects (key values must match the weights names)
   • class_ids: dictionary of class ids to be used in the messages published on the /dope/detected_objects topic (key values must match the weights names)
   • draw_colors: dictionary of object colors (key values must match the weights names)
   • model_transforms: dictionary of transforms that are applied to the pose before publishing (key values must match the weights names)
   • meshes: dictionary of mesh filenames for visualization (key values must match the weights names)
   • mesh_scales: dictionary of scaling factors for the visualization meshes (key values must match the weights names)
   • overlay_belief_images: whether to overlay the input image on the belief images published on /dope/belief_[obj_name]
   • thresh_angle: undocumented
   • thresh_map: undocumented
   • sigma: undocumented
   • thresh_points: Thresholding the confidence for object detection; increase this value if you see too many false positives, reduce it if objects are not detected.

4. Start DOPE node

   $ roslaunch dope dope.launch [config:=/path/to/my_config.yaml]  # Config file is optional; default is `config_pose.yaml`
   

Debugging

• The following ROS topics are published (assuming topic_publishing == 'dope'):

  /dope/belief_[obj_name]    # belief maps of object
  /dope/dimension_[obj_name] # dimensions of object
  /dope/pose_[obj_name]      # timestamped pose of object
  /dope/rgb_points           # RGB images with detected cuboids overlaid
  /dope/detected_objects     # vision_msgs/Detection3DArray of all detected objects
  /dope/markers              # RViz visualization markers for all objects
  

  Note: [obj_name] is in {cracker, gelatin, meat, mustard, soup, sugar}

• To debug in RViz, run rviz, then add one or more of the following displays:

  • Add > Image to view the raw RGB image or the image with cuboids overlaid
  • Add > Pose to view the object coordinate frame in 3D.
  • Add > MarkerArray to view the cuboids, meshes etc. in 3D.
  • Add > Camera to view the RGB Image with the poses and markers from above.

  If you do not have a coordinate frame set up, you can run this static transformation: rosrun tf2_ros static_transform_publisher 0 0 0 0.7071 0 0 -0.7071 world <camera_frame_id>, where <camera_frame_id> is the frame_id of your input camera messages. Make sure that in RViz's Global Options, the Fixed Frame is set to world. Alternatively, you can skip the static_transform_publisher step and directly set the Fixed Frame to your <camera_frame_id>.

• If rosrun does not find the package ([rospack] Error: package 'dope' not found), be sure that you called source devel/setup.bash as mentioned above. To find the package, run rospack find dope.

YCB 3D Models

DOPE returns the poses of the objects in the camera coordinate frame. DOPE uses the aligned YCB models, which can be obtained using NVDU (see the nvdu_ycb command).

HOPE 3D Models

We introduce new toy 3d models that you download here. The folders are arranged like the YCB 3d models organization. You can buy the real objects using the following links set 1, set 2, set 3, and set 4.

The HOPE dataset can be found here and is also part of the BOP challenge.

How to cite DOPE

If you use this tool in a research project, please cite as follows:

@inproceedings{tremblay2018corl:dope,
 author = {Jonathan Tremblay and Thang To and Balakumar Sundaralingam and Yu Xiang and Dieter Fox and Stan Birchfield},
 title = {Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects},
 booktitle = {Conference on Robot Learning (CoRL)},
 url = "https://arxiv.org/abs/1809.10790",
 year = 2018
}

License

Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license.

Acknowledgment

Thanks to Jeffrey Smith ([email protected]) for creating the Docker image.

Contact

Jonathan Tremblay ([email protected]), Stan Birchfield ([email protected])