GVINS

GVINS: Tightly Coupled GNSS-Visual-Inertial Fusion for Smooth and Consistent State Estimation. paper link

GVINS is a non-linear optimization based system that tightly fuses GNSS raw measurements with visual and inertial information for real-time and drift-free state estimation. By incorporating GNSS pseudorange and Doppler shift measurements, GVINS is capable to provide smooth and consistent 6-DoF global localization in complex environment. The system framework and VIO part are adapted from VINS-Mono. Our system contains the following features:

• global 6-DoF estimation in ECEF frame;
• multi-constellation support (GPS, GLONASS, Galileo, BeiDou);
• online local-ENU frame alignment;
• global pose recovery in GNSS-unfriendly or even GNSS-denied area.

1. Prerequisites

1.1 C++11 Compiler

This package requires some features of C++11.

1.2 ROS

This package is developed under ROS Kinetic environment.

1.3 Eigen

Our code uses Eigen 3.3.3 for matrix manipulation.

1.4 Ceres

We use ceres 1.12.0 to solve the non-linear optimization problem.

1.5 gnss_comm

This package also requires gnss_comm for ROS message definitions and some utility functions. Follow those instructions to build the gnss_comm package.

2. Build GVINS

Clone the repository to your catkin workspace (for example ~/catkin_ws/):

cd ~/catkin_ws/src/
git clone https://github.com/HKUST-Aerial-Robotics/GVINS.git

Then build the package with:

cd ~/catkin_ws/
catkin_make
source ~/catkin_ws/devel/setup.bash

3. Run GVINS with our dataset

Download our GNSS-Visual-Inertial dataset as described in the next section. Then launch GVINS via:

roslaunch gvins visensor_f9p.launch

Subscribe /gvins/gnss_enu_path in your rviz and play the bag:

rosbag play sports_field.bag

4. GNSS-Visual-Inertial dataset

We published our GNSS-Visual-Inertial dataset at rosbag_1 and rosbag_2. The visual and inertial data are collected using a VI-Sensor, and the GNSS raw measurement is provided by a u-blox ZED-F9P receiver. The RTCM stream from a 3km-away base station is fed to the GNSS receiver for RTK solution. In addition, the time synchronization between VI-Sensor and ZED-F9P is achieved via hardware trigger.

5. Acknowledgements

The system framework and VIO part are adapted from VINS-Mono. We use camodocal to model the camera and ceres to solve the optimization problem.

6. Licence

The source code is released under GPLv3 license.