SuperSLAM: Open Source System for Deep Learning based Visual SLAM

Alpha Software

SuperSLAM is a deep learning based visual SLAM system that combines learned feature detection and matching with a classical SLAM pipeline.

Installation

Local Installation

Easiest way to get started would be the setup.sh script.

See INSTALL.md for more information.

Docker/Podman Installation

Using Docker

1. Build the Docker image:

docker build -t superslam .

2. Run the container with GPU support:

docker run --gpus all -it --rm superslam

3. For development with mounted source code:

docker run --gpus all -it --rm \
  -v $(pwd):/workspace \
  -w /workspace \
  superslam

Using Podman

1. Build the Podman image:

podman build -t superslam .

2. Run the container with GPU support:

podman run --security-opt=label=disable --device /dev/dri:/dev/dri --device /dev/nvidia0:/dev/nvidia0 --device /dev/nvidiactl:/dev/nvidiactl --device /dev/nvidia-uvm:/dev/nvidia-uvm -it --rm superslam

3. For development with mounted source code:

podman run --security-opt=label=disable --device /dev/dri:/dev/dri --device /dev/nvidia0:/dev/nvidia0 --device /dev/nvidiactl:/dev/nvidiactl --device /dev/nvidia-uvm:/dev/nvidia-uvm -it --rm \
  -v $(pwd):/workspace \
  -w /workspace \
  superslam

Build and run

git clone https://github.com/adityamwagh/SuperSLAM.git
cd SuperSLAM
sh ./build.sh

This will create libSuperSLAM.so in the lib folder and the executables mono_kitti_rerun and stereo_kitti_rerun in appropriate subfolders in examples folder.

Troubleshooting

• CUDA Errors: Ensure your NVIDIA drivers and CUDA toolkit are correctly installed.
• ROS 2 Issues: Verify that the ROS 2 environment is sourced correctly.
• Missing Dependencies: Double-check that all dependencies listed above are installed.

Contributing

Contributions to the SuperSLAM project are welcome! Please ensure that any changes are well-documented and tested.

License

This project is licensed under the LGPL.

For any questions or issues, please open an issue on the GitHub repository.

Star History

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Acknowledgements

Lot of code is borrowed from these repositories. Thanks to the authors for opensourcing them!

• SuperPoint
• SuperGlue
• TensorRT
• AirVO
• ORB_SLAM2
• superslam
• SuperPoint-SuperGlue-TensorRT