VINS-Fusion

An optimization-based multi-sensor state estimator

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

Release Note

• v1.0.1: replace map_slam with map

1. Prerequisites

1.2. Ceres Solver

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

Follow Ceres Installation.

Compilation

catkin_make install --source src/VINS-Fusion --build build/vins_fusion

Launch

3.1 Monocualr camera + IMU

    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

3.2 Stereo cameras + IMU

roslaunch vins_fusion simulation.launch

3.3 Stereo cameras

    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

6. Run with your devices

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.

6.2 Camera calibration

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

License

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>.