This package allows to model with GMM a two dimensional position distribution and one dimensional orientation distribution for different objects. The software architecture supports to use different kitchens, tables, humans and contexts for learning different position and orientation distributions. The GMMs are learned from one CSV file, which must be saved in the resource folder and its name should be configured in the launch file. Moreover, the launch file allows to change the CSV file feature names, visualizations options during runtime and more. This package already contains a data set for the iai_kitchen in iai_maps. Lastly, this packages supports ROS kinetic and melodic.
This python package needs atleast Python 3.6 and the following packages:
pip install --upgrade pip
sudo apt-get install python-catkin-pkg python3-pip
pip uninstall em
pip3 install pyyaml empy
python3.6 -m pip install numpy scipy pandas matplotlib seaborn sklearn
pip install --upgrade pip
sudo apt-get install python-catkin-pkg python3-pip
pip uninstall em
pip install empy
pip3 install numpy scipy pandas matplotlib seaborn sklearn
After that this package can be built with catkin_make in your ROS workspace.
To start the ROS service simply include the given launch file in your main launch file or start it with:
roslaunch costmap_learning costmap_learning_with_params.launch
The ROS-Interface has two services called GetCostmap and GetSymbolicLocation. The service GetSymbolicLocation
allows to return the symbolic storage or destination location of given object type. The service GetCostmap then
allows to return the parameters of the learned GMMs for a given object type and location.
The request takes a CRAM object_type (e.g. "BOWL"), context (e.g. "TABLE-SETTING"), name (e.g. "THOMAS"), environment name kitchen (e.g. "KITCHEN"), table name table_id (e.g. "rectangular_table") and boolean storage saving if the requested symbolic location should the storage or destination location of the object.
The response is the requested location saved as a string.
The request takes a CRAM object_type (e.g. "BOWL"), context (e.g. "TABLE-SETTING"), name (e.g. "THOMAS"), environment name kitchen (e.g. "KITCHEN"), table name table_id (e.g. "rectangular_table") and location string saving for which location the costmap should be returned. Optionally, the request can include the x and y coordinates (placed_x_object_positions and placed_y_object_positions) of already placed objects, aswell as their object types as string (placed_object_types).
The response returns the means, covariances and weights (means, covs, weights) of the positional GMMs. Moreover, the response returns the boundry boxes of these distributions, which are rougly away from each mean. The boundry boxes are encoded with geometry_msgs/Points in
bottem_lefts and floats in widths and heights. The widhts and heights values of these boundry boxes are saved in meters. Lastly, the parameter angles returns the one dimensional orientation distribution by returning the means and covarinaces repeatably for each positional distribution.