install.md

Edited by Shehan

Installing and Preparing the Human-Baxter Collaboration Framework

The following instructions are adapted in parts from here.

The Human-Baxter collaboration framework is implemented as a ROS package. It has been tested with a development workstation with Ubuntu 14.04 and ROS Indigo. We recommend a setup as follows, as illustrated below:

• 1 development workstation running Ubuntu 14.04, having a USB 3.0 port and a GPU with CUDA library version 7+
• [optional] 1 PC running Windows, having a USB 3.0 port
• [optional] 1 Baxter research robot and a table to pick objects from or put them onto
• [optional] 1 Kinect V2 sensor
• [optional] 1 human collaborator

Note: If you have Ubuntu, ROS and and the Baxter SDK dependencies already installed, you only need to perform steps 3, 5 and 6 to clone, install and setup the Human-Baxter collaboration framework!

Step 1: Install Ubuntu

Follow the standard Ubuntu Installation Instructions for 14.04 (Desktop).

Step 2: Install ROS Indigo

Configure your Ubuntu repositories to allow "restricted," "universe," and "multiverse."

Setup your sources.list

$ sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu trusty main" > /etc/apt/sources.list.d/ros-latest.list'

Setup your keys

$ wget http://packages.ros.org/ros.key -O - | sudo apt-key add -

Verify latest debians, install ROS Indigo Desktop Full and rosinstall

$ sudo apt-get update
$ sudo apt-get install ros-indigo-desktop-full
$ sudo rosdep init
$ rosdep update
$ sudo apt-get install python-rosinstall

Step 3: Create ROS workspace

In the following, we assume that the framework is installed in a catkin workspace called $WS_HBCF.

$ mkdir -p $WS_HBCF/src
$ source /opt/ros/indigo/setup.bash
$ cd $WS_HBCF
$ catkin_make
$ catkin_make install

Step 4: Install Baxter SDK-, Baxter simulator and Kinect dependencies

$ sudo apt-get update
$ sudo apt-get install git-core python-argparse python-wstool python-vcstools python-rosdep ros-indigo-control-msgs ros-indigo-joystick-drivers
$ sudo apt-get install gazebo2 ros-indigo-qt-build ros-indigo-driver-common ros-indigo-gazebo-ros-control ros-indigo-gazebo-ros-pkgs ros-indigo-ros-control ros-indigo-control-toolbox ros-indigo-realtime-tools ros-indigo-ros-controllers ros-indigo-xacro python-wstool ros-indigo-tf-conversions ros-indigo-kdl-parser

To install the Microsoft Kinect V2 for Linux we need the libfreenect2 library, which we will build from source and install into ~/freenect2:

$ sudo apt-get install libturbojpeg libopenni2-dev
$ git clone https://github.com/OpenKinect/libfreenect2.git ~/libfreenect2
$ cd ~/libfreenect2/depends
$ ./download_debs_trusty.sh
$ sudo dpkg -i debs/libglfw3*deb
$ sudo apt-get install -f
$ cd ~/libfreenect2
$ mkdir build
$ cd build
$ cmake .. -DENABLE_CXX11=ON -DCMAKE_INSTALL_PREFIX=$HOME/freenect2
$ make
$ make install
$ sudo cp ../platform/linux/udev/90-kinect2.rules /etc/udev/rules.d/

To verify the installation, plug in the Kinect V2 into an USB 3 port and run the ./bin/Protonect test program.

Step 5: Install this package and its dependencies

Using the wstool workspace tool, you will checkout all required Github repositories into your ROS workspace source directory. The ROS bridge for the Kinect V2 will be installed separately.

$ cd $WS_HBCF/src
$ wstool init .
$ wstool merge https://raw.githubusercontent.com/BRML/baxter_rosinstall/master/baxter_pnp.rosinstall
$ wstool update
# now install dependencies for iai_kinect2
$ cd iai_kinect2
$ source /opt/ros/indigo/setup.bash
$ rosdep install -r --from-paths .
$ cd $WS_HBCF
$ catkin_make -DCMAKE_BUILD_TYPE="Release"
$ catkin_make install

Note: rosdep will output errors about not being able to locate [kinect2_bridge] and [depth_registration]. That is fine, since those are part of the iai_kinect2 package.

We now install R-FCN for object detection using deep neural networks into $DEVEL (default in the framework settings is ~/software). Please refer to the Caffe installation instructions if anything is unclear.

$ mkdir $DEVEL
$ cd $DEVEL
# clone R-FCN and Microsoft Caffe commit 1a2be8e
$ git clone https://github.com/Orpine/py-R-FCN.git
$ cd $DEVEL/py-R-FCN
$ git clone https://github.com/Microsoft/caffe.git
$ cd $DEVEL/py-R-FCN/caffe
$ git reset --hard 1a2be8e
# build the Cython modules
$ cd $DEVEL/py-R-FCN/lib
$ make
# with your Makefile.config in place, build Caffe
$ cd $DEVEL/py-R-FCN/caffe
$ make -j8 && make pycaffe

Note: Caffe must be built with support for Python layers! That is, in your Makefile.config, make sure to have this line uncommented

WITH_PYTHON_LAYER := 1

Optionally, faster R-CNN and MNC can be installed in a quite similar fashion. Please refer to the respective installation instructions for details.

Step 6: Configure Baxter communication/ROS workspace

The baxter.sh script is a convenient script which allows for intuitive modification of the core ROS environment components. This user edited script will allow for the quickest and easiest ROS setup. Further information and a detailed description is available on the baxter.sh page.

Download the baxter.sh script

$ cd $WS_HBCF
$ wget https://github.com/RethinkRobotics/baxter/raw/master/baxter.sh
$ chmod u+x baxter.sh

Customize the baxter.sh script

Using your favorite editor, edit the baxter.sh shell script making the necessary modifications to describe your development workstation.

• Edit the baxter_hostname field to match the hostname of your Baxter robot.
• Edit either the your_ip or the your_hostname field to match the IP or hostname of your development workstation. Only one of those fields can be active at a time. The other variable should be commented out!

Initialize your SDK environment

$ cd $WS_HBCF
$ . baxter.sh

Verify environment

To verify that all your changes are applied correctly, perform

$ env | grep ROS

The important fields at this point are

• ROS_MASTER_URI (this should now contain your robot's hostname)
• ROS_IP or ROS_HOSTNAME (this should now contain your development workstation's ip address or hostname. The unused field should not be available!)