VINS-Fusion is an optimization-based multi-sensor state estimator, which achieves accurate self-localization for autonomous applications (drones, cars, and AR/VR). VINS-Fusion is an extension of VINS-Mono, which supports multiple visual-inertial sensor types (mono camera + IMU, stereo cameras + IMU, even stereo cameras only). We also show a toy example of fusing VINS with GPS. Features:

• multiple sensors support (stereo cameras / mono camera+IMU / stereo cameras+IMU)
• online spatial calibration (transformation between camera and IMU)
• online temporal calibration (time offset between camera and IMU)
• visual loop closure

We are the top open-sourced stereo algorithm on KITTI Odometry Benchmark (12.Jan.2019).

Authors: Tong Qin, Shaozu Cao, Jie Pan, Peiliang Li, and Shaojie Shen from the Aerial Robotics Group, HKUST

Videos:

Related Paper: (paper is not exactly same with code)

• Online Temporal Calibration for Monocular Visual-Inertial Systems, Tong Qin, Shaojie Shen, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS, 2018), best student paper award pdf

• VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator, Tong Qin, Peiliang Li, Shaojie Shen, IEEE Transactions on Robotics pdf

If you use VINS-Fusion for your academic research, please cite our related papers. bib

Ubuntu 64-bit 16.04 or 18.04. ROS Kinetic or Melodic. ROS Installation

Follow Ceres Installation.

Clone the repository and catkin_make:

    cd ~/catkin_ws/src
    git clone https://github.com/HKUST-Aerial-Robotics/VINS-Fusion.git
    cd ../
    catkin_make
    source ~/catkin_ws/devel/setup.bash

(if you fail in this step, try to find another computer with clean system or reinstall Ubuntu and ROS)

Download EuRoC MAV Dataset to YOUR_DATASET_FOLDER. Take MH_01 for example, you can run VINS-Fusion with three sensor types (monocular camera + IMU, stereo cameras + IMU and stereo cameras). Open four terminals, run vins odometry, visual loop closure(optional), rviz and play the bag file respectively. Green path is VIO odometry; red path is odometry under visual loop closure.

    roslaunch vins vins_rviz.launch
    rosrun vins vins_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_mono_imu_config.yaml 
    (optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_mono_imu_config.yaml 
    rosbag play YOUR_DATASET_FOLDER/MH_01_easy.bag

    roslaunch vins vins_rviz.launch
    rosrun vins vins_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_stereo_imu_config.yaml 
    (optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_stereo_imu_config.yaml 
    rosbag play YOUR_DATASET_FOLDER/MH_01_easy.bag

    roslaunch vins vins_rviz.launch
    rosrun vins vins_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_stereo_config.yaml 
    (optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_stereo_config.yaml 
    rosbag play YOUR_DATASET_FOLDER/MH_01_easy.bag

Download KITTI Odometry dataset to YOUR_DATASET_FOLDER. Take sequences 00 for example, Open two terminals, run vins and rviz respectively. (We evaluated odometry on KITTI benchmark without loop closure funtion)

    roslaunch vins vins_rviz.launch
    (optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VINS-Fusion/config/kitti_odom/kitti_config00-02.yaml
    rosrun vins kitti_odom_test ~/catkin_ws/src/VINS-Fusion/config/kitti_odom/kitti_config00-02.yaml YOUR_DATASET_FOLDER/sequences/00/ 

Download KITTI raw dataset to YOUR_DATASET_FOLDER. Take 2011_10_03_drive_0027_synced for example. Open three terminals, run vins, global fusion and rviz respectively. Green path is VIO odometry; blue path is odometry under GPS global fusion.

    roslaunch vins vins_rviz.launch
    rosrun vins kitti_gps_test ~/catkin_ws/src/VINS-Fusion/config/kitti_raw/kitti_10_03_config.yaml YOUR_DATASET_FOLDER/2011_10_03_drive_0027_sync/ 
    rosrun global_fusion global_fusion_node

Download car bag to YOUR_DATASET_FOLDER. Open four terminals, run vins odometry, visual loop closure(optional), rviz and play the bag file respectively. Green path is VIO odometry; red path is odometry under visual loop closure.

    roslaunch vins vins_rviz.launch
    rosrun vins vins_node ~/catkin_ws/src/VINS-Fusion/config/vi_car/vi_car.yaml 
    (optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VINS-Fusion/config/vi_car/vi_car.yaml 
    rosbag play YOUR_DATASET_FOLDER/car.bag

VIO is not only a software algorithm, it heavily relies on hardware quality. For beginners, we recommend you to run VIO with professional equipment, which contains global shutter cameras and hardware synchronization.

Write a config file for your device. You can take config files of EuRoC and KITTI as the example.

VINS-Fusion support several camera models (pinhole, mei, equidistant). You can use camera model to calibrate your cameras. We put some example data under /camera_models/calibrationdata to tell you how to calibrate.

cd ~/catkin_ws/src/VINS-Fusion/camera_models/camera_calib_example/
rosrun camera_models Calibrations -w 12 -h 8 -s 80 -i calibrationdata --camera-model pinhole

To further facilitate the building process, we add docker in our code. Docker environment is like a sandbox, thus makes our code environment-independent. To run with docker, first make sure ros and docker are installed on your machine. Then add your account to docker group by sudo usermod -aG docker $YOUR_USER_NAME. Relaunch the terminal or logout and re-login if you get Permission denied error, type:

cd ~/catkin_ws/src/VINS-Fusion/docker
make build

Note that the docker building process may take a while depends on your network and machine. After VINS-Fusion successfully built, you can run vins estimator with script run.sh. Script run.sh can take several flags and arguments. Flag -k means KITTI, -l represents loop fusion, and -g stands for global fusion. You can get the usage details by ./run.sh -h. Here are some examples with this script:

# Euroc Monocualr camera + IMU
./run.sh ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_mono_imu_config.yaml

# Euroc Stereo cameras + IMU with loop fusion
./run.sh -l ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_mono_imu_config.yaml

# KITTI Odometry (Stereo)
./run.sh -k ~/catkin_ws/src/VINS-Fusion/config/kitti_odom/kitti_config00-02.yaml YOUR_DATASET_FOLDER/sequences/00/

# KITTI Odometry (Stereo) with loop fusion
./run.sh -kl ~/catkin_ws/src/VINS-Fusion/config/kitti_odom/kitti_config00-02.yaml YOUR_DATASET_FOLDER/sequences/00/

#  KITTI GPS Fusion (Stereo + GPS)
./run.sh -kg ~/catkin_ws/src/VINS-Fusion/config/kitti_raw/kitti_10_03_config.yaml YOUR_DATASET_FOLDER/2011_10_03_drive_0027_sync/

In Euroc cases, you need open another terminal and play your bag file. If you need modify the code, simply re-run ./run.sh with proper auguments after your changes.

We use ceres solver for non-linear optimization and DBoW2 for loop detection, a generic camera model and GeographicLib.

The source code is released under GPLv3 license.

We are still working on improving the code reliability. For any technical issues, please contact Tong Qin <qintonguavATgmail.com>.

For commercial inquiries, please contact Shaojie Shen <eeshaojieATust.hk>.