GPMP2

This library is an implementation of GPMP2 (Gaussian Process Motion Planner 2) algorithm described in Motion Planning as Probabilistic Inference using Gaussian Processes and Factor Graphs (RSS 2016). The core library is developed in C++ language, and an optional Matlab toolbox is provided. Examples are provided in Matlab scripts. A ROS interface is also available within PIPER.

GPMP2 was developed by Jing Dong and Mustafa Mukadam as part of their work at Georgia Tech Robot Learning Lab.

Prerequisites

• CMake >= 2.6 (Ubuntu: sudo apt-get install cmake), compilation configuration tool.
• Boost >= 1.50 (Ubuntu: sudo apt-get install libboost-all-dev), portable C++ source libraries.
• GTSAM >= 4.0 alpha, a C++ library that implement smoothing and mapping (SAM) framework in robotics and vision. Here we use factor graph implementations and inference/optimization tools provided by GTSAM.

Compilation & Installation

In the library folder execute:

$ mkdir build
$ cd build
$ cmake ..
$ make check  # optional, run unit tests
$ make install

Matlab Toolbox

An optional Matlab toolbox is provided to use our library in Matlab. To enable Matlab toolbox during compilation:

$ cmake -DGPMP2_BUILD_MATLAB_TOOLBOX:OPTION=ON -DGTSAM_TOOLBOX_INSTALL_PATH:PATH=/path/install/toolbox ..
$ make install

After you install the Matlab toolbox, don't forget to add /path/install/toolbox to your Matlab path.

Tested Compatibility

The gpmp2 library is designed to be cross-platform. It has been tested on Ubuntu Linux and Windows for now.

• Ubuntu: GCC 4.8 - 4.9, 5.3 - 5.4
• Windows: Visual C++ 2015 (Matlab toolbox not tested)
• Boost: 1.50 - 1.61

Questions & Bug reporting

Please use Github issue tracker to report bugs. For other questions please contact Jing Dong or Mustafa Mukadam .

Citing

If you use GPMP2 in an academic context, please cite following publications:

@inproceedings{Mukadam-IJRR-18,
  Author = {Mustafa Mukadam and Jing Dong and Xinyan Yan and Frank Dellaert and Byron Boots},
  Title = {Continuous-time {G}aussian Process Motion Planning via Probabilistic Inference},
  journal = {The International Journal of Robotics Research (IJRR)},
  volume = {37},
  number = {11},
  pages = {1319--1340},
  year = {2018}
}

@inproceedings{Dong-RSS-16,
  Author = {Jing Dong and Mustafa Mukadam and Frank Dellaert and Byron Boots},
  Title = {Motion Planning as Probabilistic Inference using {G}aussian Processes and Factor Graphs},
  booktitle = {Proceedings of Robotics: Science and Systems (RSS)},
  year = {2016}
}

@inproceedings{dong2018sparse,
  title={Sparse {G}aussian Processes on Matrix {L}ie Groups: A Unified Framework for Optimizing Continuous-Time Trajectories},
  author={Dong, Jing and Mukadam, Mustafa and Boots, Byron and Dellaert, Frank},
  booktitle={2018 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={6497--6504},
  year={2018},
  organization={IEEE}
}

License

GPMP2 is released under the BSD license, reproduced in the file LICENSE in this directory.