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Only released in EOL distros:  

Package Summary

ROS package to detect handles.

Package Summary

ROS package to detect handles.

Package Summary

ROS package to detect handles.

Overview

This package allows to localize handles in 3D point clouds. The video below demonstrates the method in a scenario where a robot has to pick up objects from a table and deposit them into a box.

If you like this package and use it in your own work, please cite our ISER2014 paper.

Requirements

  1. ROS Hydro, ROS Indigo, or ROS Kinetic

  2. Lapack (install in Ubuntu using: sudo apt-get install liblapack-dev)
  3. Openni_launch (install in Ubuntu using: sudo apt-get install ros-<ROS_VERSION>-openni-launch)

Installation

Ubuntu

  1. sudo apt-get install ros-hydro-handle-detector
  2. source /opt/ros/hydro/setup.bash

From Source

  1. Cd into the 'src' folder of your catkin workspace.
  2. Clone the github repository.
  3. Recompile your ROS workspace: $ catkin_make.

How to Localize Handles

There are two ways in which handles can be localized using this package: from a *.pcd file that contains the point cloud data, or from the point cloud data provided by a depth sensor such as an RGB-D camera, e.g., Microsoft Kinect.

Using a Point Cloud File

  1. Set the parameter 'file' in handle_detector/launch/localization_pcd_file.launch to the absolute path of some pcd file.
  2. Start roscore: $ roscore.
  3. Run the handle localization: $ roslaunch handle_detector localization_pcd_file.launch.
  4. Use RViz to visualize the results (see below): $ rosrun rviz rviz.

For the given point cloud file (handle_detector/data/table5.pcd), the output in rviz should look like this:

Output for PCD file

Using a Depth Sensor

Note: This requires an openni-compatible device, and has only been tested with an Asus Xtion Pro.

  1. Set-up one or more objects in front of the RGB-D camera, and have the RGB-D camera running.
  2. Start roscore: $ roscore.
  3. Start openni_launch: $ roslaunch openni_launch openni.launch.
  4. Run the handle localization: $ roslaunch handle_detector localization_sensor.launch.
  5. Use RViz to visualize the results (see below): $ rosrun rviz rviz.

Grasp Handles Using Localization Information

  1. Follow the steps described in (4.1) or (4.2).
  2. Subscribe to the ROS topic /localization/handle_list to get a list of handles.
  3. This list gives the pose, radius, extent, major axis, and normal axis for each affordance (cylindrical shell) in every handle.
  4. To see the structure of the messages published here, use: $ rosmsg show handle_detector/nameOfMessage.
  5. Use the given localization information to decide for a target handle, and grasp it using your robot.

Visualization in RViz

There are ready-to-use configuration files in the rviz folder.

  1. Show input point cloud: add a PointCloud2 and set the topic to /localization/point_cloud.

  2. Show all affordances: add a MarkerArray and set the topic to /localization/visualization_all_affordances.

  3. Show all handles: add a MarkerArray and set the topic to /localization/visualization_all_handles.

  4. Show handle i (i = 0, 1, ...): add a MarkerArray and set the topic to /localization/visualization_handle_i.

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Commands

Published ROS Topics

ROS Launch Parameters

Affordance Localization

file
the location of the *.pcd file on the file system (absolute path)
target_radius
the radius of the target handle
target_radius_error
the error permitted on the target radius
affordance_gap
the size of the gap around the affordance
sample_size
the number of point neighborhoods to be sampled from the point cloud
use_clearance_filter
whether the clearance filter (enough gap around affordance) is used
use_occlusion_filter
whether the occlusion filter is used
curvature_estimator
the method that is used to estimate curvature (0: Taubin Quadric Fitting, 1: Principal Component Analysis, 2: Normals)
point_cloud_source
the source of the point cloud (0: *.pcd file, 1: range sensor)
update_interval
the interval in seconds at which the algorithm is repeated
workspace_limits (6 parameters)
the minimum and maximum values of the workspace in three dimensions
num_threads
the number of CPU threads to use in Taubin Quadric Fitting

Handle Localization

alignment_runs
the number of times that handles are searched for
alignment_min_inliers
the minimum number of aligned affordances in a handle
alignment_dist_radius
the axis distance below which two affordances are considered to belong to the same handle
alignment_orient_radius
the orientation distance below which two affordances are considered to belong to the same handle
alignment_radius_radius
the radius difference below which two affordances are considered to belong to the same handle

Importance Sampling

The following parameters are only required if the importance sampling is launched.

num_iterations
the number of iterations for which the search is repeated
num_samples
the number of samples used per iteration
num_init_samples
the number of initial samples
sampling_method
the method that is used for drawing samples from the cloud (Sum of Gaussians or Maximum of Gaussians)
visualize_steps
whether each iteration is visualized (uses pcl_viewer)

Citation

If you like this package and use it in your own work, please cite our ISER2014 paper:

Andreas ten Pas and Robert Platt. Localizing Handle-Like Grasp Affordances in 3-D Points Clouds Using Taubin Quadric Fitting. International Symposium on Experimental Robotics (ISER), Morocco, June 2014.


2024-03-16 12:36