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https://github.com/Mobile-Robotics-W20-Team-9/UMICH-NCLT-SLAP.git
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making it look nicer
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27
docs/home.md
27
docs/home.md
@@ -91,49 +91,76 @@ Install the following dependencies
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2. `pip install numpy matplotlib open3d-python progressbar pyquaternion transforms3d scipy scikit-image networkx psutil`
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2. `pip install numpy matplotlib open3d-python progressbar pyquaternion transforms3d scipy scikit-image networkx psutil`
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3. Install required dependencies including ray_tracing, pybind11, pytest, and Catch2
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3. Install required dependencies including ray_tracing, pybind11, pytest, and Catch2
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`git clone https://github.com/catchorg/Catch2.git`
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`git clone https://github.com/catchorg/Catch2.git`
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`mkdir Catch2/build`
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`mkdir Catch2/build`
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`cd Catch2/build`
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`cd Catch2/build`
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`cmake ..`
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`cmake ..`
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`make -j8`
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`make -j8`
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`sudo make install`
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`sudo make install`
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`pip install pytest`
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`pip install pytest`
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`git clone https://github.com/pybind/pybind11.git`
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`git clone https://github.com/pybind/pybind11.git`
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`mkdir pybind11/build`
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`mkdir pybind11/build`
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`cd pybind11/build`
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`cd pybind11/build`
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`cmake ..`
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`cmake ..`
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`make -j8`
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`make -j8`
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`sudo make install`
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`sudo make install`
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`git clone https://github.com/acschaefer/ray_tracing.git`
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`git clone https://github.com/acschaefer/ray_tracing.git`
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`mkdir ray_tracing/build && cd ray_tracing/build`
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`mkdir ray_tracing/build && cd ray_tracing/build`
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`cmake ..`
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`cmake ..`
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`make -j8`
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`make -j8`
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`sudo make install`
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`sudo make install`
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4. Download raw data from [NCLT](http://robots.engin.umich.edu/nclt/) to run code from scratch OR use our [prebuilt maps](https://drive.google.com/drive/folders/1cFf0q76xyul4nbShm-GwDNxFwYh1Bkzx?usp=sharing)
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4. Download raw data from [NCLT](http://robots.engin.umich.edu/nclt/) to run code from scratch OR use our [prebuilt maps](https://drive.google.com/drive/folders/1cFf0q76xyul4nbShm-GwDNxFwYh1Bkzx?usp=sharing)
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4.1 The raw data can be downloaded from the website directly or using the downloader.py. If you are using the prebuilt maps, skip this step.
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4.1 The raw data can be downloaded from the website directly or using the downloader.py. If you are using the prebuilt maps, skip this step.
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`cd src/dataset/`
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`cd src/dataset/`
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`python download.py --date="2012-01-15" --vel --gt --gt_cov --sensor`
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`python download.py --date="2012-01-15" --vel --gt --gt_cov --sensor`
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`cd nclt/sensors/<date>`
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`cd nclt/sensors/<date>`
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`tar xzf <sensors_file>`
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`tar xzf <sensors_file>`
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`cd ../velodyne`
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`cd ../velodyne`
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`tar xzf <velodyne_file>`
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`tar xzf <velodyne_file>`
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Repeat this for any interested datasets. We have maps made for 2012-01-15, 2012-01-08, 2013-04-05, and 2013-01-10.
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Repeat this for any interested datasets. We have maps made for 2012-01-15, 2012-01-08, 2013-04-05, and 2013-01-10.
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5. In ncltpoles.py we need to make a couple changes
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5. In ncltpoles.py we need to make a couple changes
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`cd src/polex/poles/`
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`cd src/polex/poles/`
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Open and edit ncltpoles.py with your favorite editor.
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Open and edit ncltpoles.py with your favorite editor.
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There are two # TODO comments. In the first one change 2020-04 to <year>-<month>. In the second one change the session to your desired session date. If you would like to run the exact experiment we did, do not change this date.
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There are two # TODO comments. In the first one change 2020-04 to <year>-<month>. In the second one change the session to your desired session date. If you would like to run the exact experiment we did, do not change this date.
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6. In pynclt.py we need to make a couple changes
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6. In pynclt.py we need to make a couple changes
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`cd src/polex/poles`
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`cd src/polex/poles`
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Open and edit pynclt.py with your favorite editor.
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Open and edit pynclt.py with your favorite editor.
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There are two # TODO comments. In the first one, change paths to the appropraite directories on your machine. In the second one, comment our the sessions you are not using.
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There are two # TODO comments. In the first one, change paths to the appropraite directories on your machine. In the second one, comment our the sessions you are not using.
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7. Run it. WARNING this can take anywhere from 1 - 10 hours for 4 datasets.
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7. Run it. WARNING this can take anywhere from 1 - 10 hours for 4 datasets.
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`cd src/polex/poles`
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`cd src/polex/poles`
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`python2 ncltpoles.py`
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`python2 ncltpoles.py`
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