Ivan Dryanovski
[email protected]
Copyright (C) 2013, City University of New York
CCNY Robotics Lab
http://robotics.ccny.cuny.edu/
The stack contains ROS applications for visual odometry and mapping using RGB-D cameras. The applications are built on top of the rgbdtools library.
This code is at an experimental stage, and licensed under the GPLv3 license.
Create a directory where you want the package downloaded (ex. ~/ros),
and make sure it's added to your$ROS_PACKAGE_PATH.
If you don't have git installed, do so:
sudo apt-get install git-core
Download the stack from our repository:
git clone https://github.com/ccny-ros-pkg/ccny_rgbd_tools.git
Install any dependencies using rosdep.
rosdep install ccny_rgbd_tools
Alternatively, you can manually install the dependencies by
sudo apt-get install libsuitesparse-dev
Compile the stack:
rosmake ccny_rgbd_tools
If you get an error compiling ccny_g2o, it might be because of an incompatible g2o installation. Try removing libg2o:
sudo apt-get remove ros-fuerte-libg2o
sudo apt-get remove ros-groovy-libg2o
Connect your RGB-D camera and launch the Openni device. The openni_launch file will start the driver and the processing nodelets.
roslaunch ccny_openni_launch openni.launch
For faster performace, consider using dynamic reconfigure to change the sampling rate of
the rgbd_image_proc nodelet. For example, setting it to to 0.5 will downsample the images by a factor of 2.
Next, launch the visual odometry:
roslaunch ccny_rgbd vo+mapping.launch
Finally, launch rviz.
rosrun rviz rviz
For convenience, you can load the ccny_rgbd/launch/rviz.cfg file.
If you use this system in your reasearch, please cite the following paper:
Ivan Dryanovski, Roberto G. Valenti, Jizhong Xiao. Fast Visual Odometry and Mapping from RGB-D Data. 2013 International Conference on Robotics and Automation (ICRA2013).
Documentation:
Videos:
- Visual odometry & 3D mapping: http://youtu.be/YE9eKgek5pI
- Feature viewer: http://youtu.be/kNkrPuBu8JA
