SuperVINS

SuperVINS: A Real-Time Visual-Inertial SLAM Framework
for Challenging Imaging Conditions

Hongkun Luo, Yang Liu, Chi Guo, Zengke Li, Weiwei Song

Project Website | Paper (IEEE Sensors Journal) | arXiv

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📢 News

• 2026.05: Released SuperVINS 2.0 — Fixed memory leak bugs, added LightGlue-based loop closure verification (SuperPoint+LightGlue PnP), dense loop-corrected trajectory output (TUM format), and enhanced RViz visualization.
• 2025.03: Published in IEEE Sensors Journal.
• 2025.03.07: Released SuperVINS 1.0 with SuperPoint+LightGlue front-end.
• 2024.10.06: Released base code.
• 2024.08.07: Added demo.
• 2024.07.31: Published preprint. arXiv

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🎬 Demo

Full demo video: resources/video.mp4

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🔍 Overview

SuperVINS is a real-time Visual-Inertial SLAM system built upon VINS-Fusion, replacing traditional handcrafted features with deep-learning-based feature extraction and matching throughout the entire pipeline:

Module	VINS-Fusion (Original)	SuperVINS
Feature Extraction	Shi-Tomasi corners	SuperPoint (learned keypoints + descriptors)
Feature Matching	Optical flow (KLT)	LightGlue (learned matcher via ONNX Runtime GPU)
Loop Detection	DBoW2 + BRIEF	DBoW3 + SuperPoint descriptors
Loop Verification	BRIEF descriptor matching	SuperPoint + LightGlue matching + PnP RANSAC

Why "SuperVINS"?

The name honors SuperPoint and VINS-Fusion — "Super" refers to the SuperPoint descriptor that runs through both front-end tracking and loop closure, while "VINS" acknowledges the visual-inertial fusion backbone.

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📦 Download Datasets

cd ~/catkin_ws/src/SuperVINS
chmod +x download_data.sh
./download_data.sh

Or manually download EuRoC MAV Dataset and place the rosbag files in your data directory.

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🐳 Quick Start (Docker, Recommended)

📋 Prerequisites

• NVIDIA GPU (tested on RTX 4060, RTX 3090)
• NVIDIA Container Toolkit
• Docker

🏗️ 1. Build or Pull Docker Image

# Option A (Recommended): Pull pre-built image (~7.18 GB, CUDA 11.8, Ubuntu 20.04，Noetic)
docker pull luohongkun0715/supervins:latest

# Option B: Build from Dockerfile (very slow)
cd SuperVINS
docker build -f docker/Dockerfile -t supervins:latest .

🚀 2. Run Container

xhost +local:root && \
docker run -it \
  --gpus all \
  --network=host \
  --privileged \
  -v /tmp/.X11-unix:/tmp/.X11-unix \
  -e DISPLAY=$DISPLAY \
  -v <YOUR_SUPERVINS_PATH>:/root/catkin_ws/src/SuperVINS \
  -v <YOUR_DATA_PATH>:/data \
  --name supervins_work \
  -w /root/catkin_ws \
  supervins:latest

Replace <YOUR_SUPERVINS_PATH> with your local SuperVINS repo path, and <YOUR_DATA_PATH> with your dataset directory.

For example:

xhost +local:root && \
docker run -it \
  --gpus all \
  --network=host \
  --privileged \
  -v /tmp/.X11-unix:/tmp/.X11-unix \
  -e DISPLAY=$DISPLAY \
  -v /home/lhk/workspace/SuperVINS:/root/catkin_ws/src/SuperVINS \
  -v /home/lhk/data:/data \
  --name supervins_work \
  -w /root/catkin_ws \
  supervins:latest

🔨 3. Build Inside Container

cd ~/catkin_ws
source /opt/ros/noetic/setup.bash
source devel/setup.bash
catkin_make -DCMAKE_BUILD_TYPE=Release -j8

▶️ 4. Run

Open 4 terminals (or use tmux) inside the container:

# Terminal 1: RViz visualization
cd ~/catkin_ws
source devel/setup.bash
roslaunch supervins supervins_rviz.launch

# Terminal 2: VIO front-end
cd ~/catkin_ws
source devel/setup.bash
rosrun supervins supervins_node ~/catkin_ws/src/SuperVINS/config/euroc/euroc_mono_imu_config.yaml

# Terminal 3: Loop fusion
cd ~/catkin_ws
source devel/setup.bash
rosrun supervins_loop_fusion supervins_loop_fusion_node ~/catkin_ws/src/SuperVINS/config/euroc/euroc_mono_imu_config.yaml

# Terminal 4: Play dataset
cd ~/catkin_ws
source devel/setup.bash
rosbag play /data/V2_01_easy.bag

🗺️ Trajectory Visualization

Color	Topic	Description
Green	/supervins_estimator/path	Real-time VIO trajectory (no loop closure)
Orange	/supervins_loop_fusion/loop_corrected_path	Dense loop-corrected trajectory (smooth, per-frame)
Red	/supervins_loop_fusion/pose_graph_path	Pose graph optimized keyframe trajectory

The loop-corrected trajectory is automatically saved in TUM format to <output_path>/loop_corrected_tum.txt for offline evaluation with EVO:

evo_ape tum groundtruth.txt loop_corrected_tum.txt -va --plot

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🔧 Build Without Docker

📋 Dependencies

Dependency	Version	Notes
Ubuntu	20.04 (64-bit)
ROS	Noetic	Installation
OpenCV	>= 4.2.0	sudo apt-get install libopencv-dev
Ceres Solver	>= 2.1.0	Installation
ONNX Runtime	1.16.3 (GPU)	Download
CUDA	>= 11.0	Required for ONNX Runtime GPU

🏗️ Build Steps

# 1. Create workspace
mkdir -p ~/catkin_ws/src && cd ~/catkin_ws/src
catkin_init_workspace

# 2. Clone
git clone https://github.com/luohongk/SuperVINS.git

# 3. Build
cd ~/catkin_ws
catkin_make -DCMAKE_BUILD_TYPE=Release

▶️ Run

# Terminal 1: RViz visualization
cd ~/catkin_ws
source devel/setup.bash
roslaunch supervins supervins_rviz.launch

# Terminal 2: VIO front-end
cd ~/catkin_ws
source devel/setup.bash
rosrun supervins supervins_node ~/catkin_ws/src/SuperVINS/config/euroc/euroc_mono_imu_config.yaml

# Terminal 3: Loop fusion
cd ~/catkin_ws
source devel/setup.bash
rosrun supervins_loop_fusion supervins_loop_fusion_node ~/catkin_ws/src/SuperVINS/config/euroc/euroc_mono_imu_config.yaml

# Terminal 4: Play dataset
cd ~/catkin_ws
source devel/setup.bash
rosbag play /data/V2_01_easy.bag

⚙️ Configuration

Key parameters in config YAML files (e.g., config/euroc/euroc_mono_imu_config.yaml):

Parameter	Description	Default
extractor_weight_path	SuperPoint ONNX model path	weights_dpl/superpoint.onnx
matcher_weight_path	LightGlue ONNX model path	weights_dpl/superpoint_lightglue_fused.onnx
matche_score_threshold	LightGlue match confidence threshold	0.5
voc_relative_path	DBoW3 vocabulary path for loop detection	ThirdParty/Voc/superpoint1.yml.gz

Path configuration in CMakeLists.txt (supervins_estimator, supervins_loop_fusion, camera_models):

set(ONNXRUNTIME_ROOTDIR "<your_onnxruntime_path>")
find_package(Ceres REQUIRED PATHS "<your_ceres_path>")

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📚 Train Vocabulary

To train a custom DBoW3 vocabulary with SuperPoint descriptors, refer to the DBoW3 official repository.

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📖 Citation

If you find SuperVINS useful in your research, please cite:

@article{luo2025supervins,
  title     = {SuperVINS: A Real-Time Visual-Inertial SLAM Framework for Challenging Imaging Conditions},
  author    = {Luo, Hongkun and Liu, Yang and Guo, Chi and Li, Zengke and Song, Weiwei},
  journal   = {IEEE Sensors Journal},
  year      = {2025},
  publisher = {IEEE},
  doi       = {10.1109/JSEN.2025.3553653}
}

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🙏 Acknowledgements

SuperVINS is built upon the following outstanding open-source projects:

• VINS-Fusion — Visual-Inertial SLAM backbone
• SuperPoint — Learned feature detector and descriptor
• LightGlue — Learned feature matcher
• LightGlue-OnnxRunner — ONNX deployment reference
• DBoW3 — Bag-of-Words loop detection

We also gratefully acknowledge the following community projects for actively testing, extending, and demonstrating SuperVINS. Feel free to reach out to their authors for related questions:

• DR-VINS — A community extension of SuperVINS tested for degraded visual scenes (low texture, illumination change, overexposure, indoor-outdoor transition)
  • Additional demos and visualizations: release demos · trajectory figures
• SuperVINS ROS2— ROS 2 implementation of SuperVINS
• AdaptiveVINS — An adaptive visual-inertial odometry extension built upon SuperVINS and VINS-Fusion, which adaptively fuses classical optical flow front-end and deep learning-based front-end to balance localization accuracy and computational overhead
• SuperVINS (Webots Adapted)— A customized fork fully adapted for Webots robot simulation, with revised camera models, simulation configs and dedicated compilation scripts

Finally, we thank the Wuhan University BRAIN Lab for supporting this project.

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📄 License

SuperVINS is released under the GNU General Public License v3.0.