Skip to content

gtrll/gpslam

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

1 Commit
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

GP-SLAM

GP-SLAM is a library implenmenting sparse Gaussian process (GP) regression for continuous-time trajectory estimation and mapping. The core library is developed by C++ language, and an optional Matlab toolbox is also provided. Examples are provided in Matlab scripts.

GP-SLAM is being developed by Jing Dong and Xinyan Yan 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 >= 1.46 (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) in robotics and vision.

Compilation & Installation

In the library folder excute:

$ mkdir build
$ cd build
$ cmake ..
$ make check  # optonal, 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 -DGPSLAM_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 your /path/install/toolbox to your Matlab path.

Compatibility

The GP-SLAM library is designed to be cross-platform, but it has been only tested on Ubuntu Linux for now.

Tested Compilers:

  • GCC 4.8, 5.4

Tested Boost version: 1.48-1.61

Linking to External Projects

We provide easy linking to external CMake projects. Add following lines to your CMakeLists.txt

find_package(gpslam REQUIRED)
include_directories(${gpslam_INCLUDE_DIR})

Questions & Bug reporting

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

Citing

If you use GP-SLAM in an academic context, please cite following publications:

@inproceedings{Yan17ras,  
  Author = "Xinyan Yan and Vadim Indelman and Byron Boots",
  journal = " Robotics and Autonomous Systems",
  Title = "Incremental Sparse {GP} Regression for Continuous-time Trajectory Estimation and Mapping",
  Year = {2017},
  pages="120-132",
  volume = {87}
}
@article{Dong17arxiv,
  author    = {Jing Dong and Byron Boots and Frank Dellaert},
  title     = {Sparse Gaussian Processes for Continuous-Time Trajectory Estimation on Matrix Lie Groups},
  journal   = {Arxiv},
  volume    = {abs/1705.06020},
  year      = {2017},
  url       = {http://arxiv.org/abs/1705.06020}
}

License

GP-SLAM is released under the BSD license, reproduced in the file LICENSE in this directory.

About

Sparse Gaussian Processes for SLAM

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published