SuperSLAM: Framework for deep learning based SLAM

(Work in Progress) SuperSLAM is a deep learning based visual SLAM system that combines recent advances in learned feature detection and matching with the mapping capabilities of ORB_SLAM2.

It utilizes SuperPoint for keypoint detection and description and SuperGlue for robust feature matching between frames. These matches are then used by ORB_SLAM2 to estimate camera poses and build a map of the environment.

Environment required

• CUDA==11.6
• TensorRT==8.4.1.5
• OpenCV>=4.0
• Eigen
• yaml-cpp
• DBoW3
• DBoW2
• Ubuntu 20.04

Installation

Clone the repository and the submodules.

git clone https://github.com/adityamwagh/SuperSLAM.git --recursive
cd SuperSLAM

Automatically Install Dependencies

sh ./install_dependencies.sh

You can use the included script to build the dependencies or install using the APT package manager.

Manually Install Dependencies

OpenCV

sudo apt-get install -y libopencv-dev

Eigen

sudo apt install libeigen3-dev

Pangolin

git clone --recursive https://github.com/stevenlovegrove/Pangolin.git

Pangolin is split into a few components so you can include just what you need. Most dependencies are optional so you can pick and mix for your needs. Rather than enforcing a particular package manager, you can use a simple script to generate a list of (required, recommended or all) packages for installation for that manager (e.g. apt, port, brew, dnf, pacman, vcpkg):

# See what package manager and packages are recommended
./scripts/install_prerequisites.sh --dry-run recommended

# install recommended prerequisites for pangolin
./scripts/install_prerequisites.sh recommended

# Configure and build
cmake -B build
cmake --build build

# with Ninja for faster builds (sudo apt install ninja-build)
cmake -B build -GNinja
cmake --build build

Acknowledgements

• SuperPoint
• SuperGlue
• TensorRT
• AirVO
• ORB_SLAM2