welcome to supereight: a high performance template octree library and a dense volumetric SLAM pipeline implementation.

This software implements the octree library and dense SLAM system presented in our paper Efficient Octree-Based Volumetric SLAM Supporting Signed-Distance and Occupancy Mapping. If you publish work that relates to this software, please cite our paper as:

@ARTICLE{VespaRAL18, author={E. Vespa and N. Nikolov and M. Grimm and L. Nardi and P. H. J. Kelly and S. Leutenegger}, journal={IEEE Robotics and Automation Letters}, title={Efficient Octree-Based Volumetric SLAM Supporting Signed-Distance and Occupancy Mapping}, year={2018}, volume={3}, number={2}, pages={1144-1151}, doi={10.1109/LRA.2018.2792537}, ISSN={}, month={April}}

The core library is released under the BSD 3-clause Licence. There are part of the this software that are released under MIT licence, see individual headers for which licence applies.

supereight is made of three main different components:

• se_core: the main header only template octree library
• se_denseslam: the volumetric SLAM pipelines presented in [1], which can be compiled in a library and used in external projects. Notice that the pipeline API exposes the discrete octree map via a shared_ptr. As the map is a template class, it needs to be instantiated correctly. You do this by defining a SE_FIELD_TYPE macro before including DenseSLAMSystem.h. The field type must be consistent with the library you are linking against. Have a look at se_denseslam and se_apps CMakeLists to see how it is done in our examples.
• se_apps: front-end applications which run the se-denseslam pipelines on given inputs or live camera.

Additionally, se_tools includes the dataset generation tool and some libraries required by se_denseslam and se_apps.

The following packages are required to build the se-denseslam library:

• CMake >= 3.10
• Eigen3
• Sophus
• OpenMP (optional)
• GTest

The benchmarking and GUI apps additionally require:

• GLut
• OpenGL
• OpenNI2
• PkgConfig/Qt5
• Python/Numpy for evaluation scripts

From the project root: make This will create a build/ folder from which cmake .. is invoked.

To run one of the apps in se_apps you need to first produce an input file. We use SLAMBench 1.0 file format (https://github.com/pamela-project/slambench). The tool scene2raw can be used to produce an input sequence from the ICL-NUIM dataset:

mkdir living_room_traj2_loop
cd living_room_traj2_loop
wget http://www.doc.ic.ac.uk/~ahanda/living_room_traj2_loop.tgz
tar xzf living_room_traj2_loop.tgz
cd ..
build/se_tools/scene2raw living_room_traj2_loop living_room_traj2_loop/scene.raw

Then it can be used as input to one of the apps

./build/se_apps/se-denseslam-sdf-main -i living_room_traj2_loop/scene.raw -s 4.8 -p 0.34,0.5,0.24 -z 4 -c 2 -r 1 -k 481.2,-480,320,240  > benchmark.log