sgolodetz / smg-pyoctomap

smg-pyoctomap

This Python package provides Python bindings for Octomap.

It is a submodule of smglib, the open-source Python framework associated with our drone research in the Cyber-Physical Systems group at the University of Oxford.

Installation (as part of smglib)

Note: Please read the top-level README for smglib before following these instructions.

1. Open the terminal.

2. Build and install Octomap.

   i. Clone our fork of Octomap into C:/octomap:

   git clone [e-mail]:sgolodetz/octomap.git C:/octomap
   

   ii. Change to the C:/octomap directory.

   iii. Check out the heightcolouring branch.

   iv. Configure Octomap using CMake, with C:/octomap/build as the build directory. Set relevant variables as per the box below. Then build (but don't install) Octomap using Visual Studio to check that it builds cleanly.

   BUILD_DYNAMICETD3D_SUBPROJECT=no
   BUILD_OCTOVIS_SUBPROJECT=no
   BUILD_TESTING=no
   CMAKE_INSTALL_PREFIX=C:/octomap/install
   

   v. Now go back to CMake, enable BUILD_OCTOVIS_SUBPROJECT and reconfigure. This will result in an error saying that libQGLViewer cannot be found or generated. Unfortunately, this must be built using Qt Creator, so you need to download Qt. The version we used was 5.14.2, the installer for which can be found here. You then need to run Qt Creator and build C:/octomap/octovis/src/extern/QGLViewer/QGLViewer.pro.

   vi. If you go back to CMake and reconfigure, QGLViewer should now be found. However, the Qt5 directories and library paths will be detected wrongly (we want the ones from our downloaded version of Qt, not the ones in the Anaconda library). They should thus be modified accordingly (assuming that you installed Qt in C:/Qt):

   QT5CORE_LIBRARY_DEBUG=C:/Qt/5.14.2/5.14.2/msvc2017_64/lib/Qt5Cored.lib
   QT5CORE_LIBRARY_RELEASE=C:/Qt/5.14.2/5.14.2/msvc2017_64/lib/Qt5Core.lib
   Qt5Core_DIR=C:/Qt/5.14.2/5.14.2/msvc2017_64/lib/cmake/Qt5Core
   Qt5Gui_DIR=C:/Qt/5.14.2/5.14.2/msvc2017_64/lib/cmake/Qt5Gui
   Qt5OpenGL_DIR=C:/Qt/5.14.2/5.14.2/msvc2017_64/lib/cmake/Qt5OpenGL
   Qt5Widgets_DIR=C:/Qt/5.14.2/5.14.2/msvc2017_64/lib/cmake/Qt5Widgets
   Qt5Xml_DIR=C:/Qt/5.14.2/5.14.2/msvc2017_64/lib/cmake/Qt5Xml
   

   vii. Reconfigure once again, and then try regenerating the Visual Studio project.

   viii. If that works, try building and installing Octomap in Debug mode. If that works, rename the C:/octomap/install directory to install-debug. Then try building and installing Octomap in Release mode. If that works, rename the C:/octomap/install directory install-release. (In both cases, use Rebuild All when doing the build to avoid issues with stale files.)

   ix. To run C:/octomap/install-release/bin/octovis.exe, set the QT_PLUGIN_PATH environment variable (at the system level) to C:\Qt\5.14.2\5.14.2\msvc2017_64\plugins, and copy the QGLViewer and Qt5 DLLs across to the C:/octomap/install-release/bin directory. Note that setting QT_PLUGIN_PATH in this way will temporarily prevent Qt Creator from running. To fix this, you can simply remove the QT_PLUGIN_PATH environment variable again (or rename it).

3. Set (at a system level, not in the terminal) the following environment variables:

   SMGLIB_OCTOMAP_DIR=C:/octomap
   SMGLIB_Qt5_DIR=C:/Qt/5.14.2/5.14.2/msvc2017_64/lib/cmake/Qt5
   

4. Re-open the terminal, and change to the <root>/smg-pyoctomap directory.

5. Check out the master branch.

6. Activate the Conda environment, e.g. conda activate smglib.

7. Run pip install -e . to install the package.

Publications

If you build on this framework for your research, please cite the following paper:

@inproceedings{Golodetz2022TR,
author = {Stuart Golodetz and Madhu Vankadari* and Aluna Everitt* and Sangyun Shin* and Andrew Markham and Niki Trigoni},
title = {{Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV}},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
month = {October},
year = {2022}
}

Acknowledgements

This work was supported by Amazon Web Services via the Oxford-Singapore Human-Machine Collaboration Programme, and by UKRI as part of the ACE-OPS grant. We would also like to thank Graham Taylor for the use of the Wytham Flight Lab, Philip Torr for the use of an Asus ZenFone AR, and Tommaso Cavallari for implementing TangoCapture.