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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](http://www.cc.gatech.edu/~bboots3/files/GPMP2.pdf) (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](https://github.com/gtrll/piper). GPMP2 was started at Georgia Tech Robot Learning Lab, see [THANKS](THANKS.md) for contributors.
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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](http://www.cc.gatech.edu/~bboots3/files/GPMP2.pdf) (RSS 2016). The core library is developed in C++ language with an optional Python 2.7 toolbox. GPMP2 was started at the Georgia Tech Robot Learning Lab, see [THANKS](THANKS.md) for contributors.
-[GTSAM](https://github.com/borglab/gtsam) >= 4.0 alpha, a C++ library that implement smoothing and mapping (SAM) framework in robotics and vision.
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Here we use factor graph implementations and inference/optimization tools provided by GTSAM.
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Compilation & Installation
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------
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In the library folder execute:
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```
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$ mkdir build
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$ cd build
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$ cmake ..
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$ make check # optional, run unit tests
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$ make install
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```
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Matlab Toolbox
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-----
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-[Anaconda2](https://docs.anaconda.com/anaconda/install/linux/), virtual environment needed if installing python toolbox.
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-[GTSAM](https://github.com/borglab/gtsam/tree/wrap-export) == `wrap_export`, a C++ library that implements smoothing and mapping (SAM) framework in robotics and vision. Here we use the factor graph implementations and inference/optimization tools provided by GTSAM.
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An optional Matlab toolbox is provided to use our library in Matlab. To enable Matlab toolbox during compilation:
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