Skip to content

MIT-SPARK/ROBIN

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

32 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation


⚙️ Build & Installation

📦 Dependencies

ROBIN has the following dependencies:

  1. OpenMP
  2. Eigen3

Thus, run the following command:

sudo apt-get install gcc g++ build-essential libeigen3-dev cmake python3-pip python3-dev git ninja-build -y

C++ C++ Installation

Run the following commands to build the library using CMake (inside the repository root directory):

mkdir build && cd build
cmake .. && make
sudo make install

The following CMake options are provided:

BUILD_DOCS: Build documentation. Default: OFF
BUILD_TESTS: Enable testing with ctest. Default: ON
BUILD_MATLAB_BINDINGS: Build MATLAB bindings. Default: OFF
USE_ASAN: Enable address sanitizer. Default: OFF
ENABLE_DIAGNOSTIC_PRINT: Enable printing of diagnostic messages. Default: OFF

Python Python Installation

It's simple! To install Python bindings, we need basic packages as follows:

pip3 install --upgrade pip setuptools wheel scikit-build-core ninja cmake build

And then, just run in out-of-the-box (the --verbose option is only for tracking purposes):

pip3 install "git+https://github.com/MIT-SPARK/ROBIN.git#subdirectory=python" --verbose

Using this repository, you can run the following command:

pip3 install -e python/

Please refer to python/example.py for usage instructions.


Third-party Data

Some test data are from the Network Repository. For more information, please refer to:

Rossi, Ryan, and Nesreen Ahmed. "The network data repository with interactive graph analytics and visualization." Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015.

Third-party Code

Known Issues

To fix missing CXXABI errors in MATLAB:

export LD_PRELOAD=/usr/lib/gcc/x86_64-linux-gnu/7/libstdc++.so

Citations

If you find this library helpful or use it in your projects, please cite:

@InProceedings{Shi21icra-robin,
	title={{ROBIN:} a Graph-Theoretic Approach to Reject Outliers in Robust Estimation using Invariants},
	author={J. Shi and H. Yang and L. Carlone},
	booktitle={IEEE Intl. Conf. on Robotics and Automation (ICRA)},
	note = {arXiv preprint: 2011.03659},
	pdf={https://arxiv.org/pdf/2011.03659.pdf},
	year={2021}
}

and

@article{Shi22arxiv-PACE,
  author = {J. Shi and H. Yang and L. Carlone},
  title = {Optimal and Robust Category-level Perception: Object Pose and Shape Estimation from {2D and 3D} Semantic Keypoints},
  journal = {arXiv preprint: 2206.12498},
  pdf = {https://arxiv.org/pdf/2206.12498.pdf},
  Year = {2022}
}

If you are interested in more works from us, please visit our lab page here.

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages