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A Robust, Real-time, INS-Centric GNSS-Visual-Inertial Navigation System
| Date | Stars |
|---|---|
| 2026-07-24 | 684 |
| 2026-07-25 | 684 |
| 2026-07-28 | 684 |
| 2026-07-30 | 684 |
| 2026-08-06 | 684 |
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# IC-GVINS ## A Robust, Real-time, INS-Centric GNSS-Visual-Inertial Navigation System Visual navigation systems are susceptible to complex environments, while inertial navigation systems (INS) are not affected by external factors. Hence, we present IC-GVINS, a robust, real-time, INS-centric global navigation satellite system (GNSS)-visual-inertial navigation system to fully utilize the INS advantages. The Earth rotation has been compensated in the INS to improve the accuracy of high-grade inertial measurement units (IMUs). To promote the system robustness in high-dynamic conditions, the precise INS information is employed to assist the feature tracking and landmark triangulation. With a GNSS-aided initialization, the IMU, visual, and GNSS measurements are tightly fused in a unified world frame within the factor graph optimization framework. <img src="resources/overview.png" alt="overview" style="zoom: 30%;" /> **Authors:** Hailiang Tang, Xiaoji Niu, and Tisheng Zhang from the [Integrated and Intelligent Navigation (i2Nav) Group](http://www.i2nav.com/), Wuhan University. **Related Paper:** - Xiaoji Niu, Hailiang Tang, Tisheng Zhang, Jing Fan, and Jingnan Liu, “IC-GVINS: A Robust, Real-time, INS-Centric GNSS-Visual-Inertial Navigation System,” *IEEE Robotics and Automation Letters*, 2022. - Hailiang Tang, Tisheng Zhang, Xiaoji Niu, Jing Fan, and Jingnan Liu, “Impact of the Earth Rotation Compensation on MEMS-IMU Preintegration of Factor Graph Optimization,” *IEEE Sensors Journal*, 2022. **Related Video**: Click the following image to open our video on [Bilibili](https://www.bilibili.com/video/BV15a411q71D). <a href ="https://www.bilibili.com/video/BV15a411q71D"><img src="resources/cover.png" alt="cover" style="zoom: 30%;" /></a> **Contacts:** - For any technique problem, you can send an email to Dr. Hailiang Tang ([email protected]). - For Chinese users, we also provide a QQ group (481173293) for discussion. You are required to provide your organization and name. ## 1 Prerequisites ### 1.1 System and compiler We recommend you use Ubuntu 18.04 or Ubuntu 20.04 with the newest compiler (**gcc>=8.0 or clang>=6.0**). ```shell # gcc-8 sudo apt install gcc-8 g++-8 # Clang # sudo apt install clang ``` ### 1.2 Robot Operating System (ROS) Follow [ROS Melodic installation instructions for Ubuntu 18.04](https://wiki.ros.org/melodic/Installation/Ubuntu) and [ROS Noetic installation instructions for Ubuntu 20.04](http://wiki.ros.org/noetic/Installation/Ubuntu). ### 1.3 Ceres Solver with its Dependencies We use Ceres Solver to solve the non-linear least squares problem in IC-GVINS. The supported version is **Ceres Solver 2.0.0 or 2.1.0**. Please follow [Ceres installation instructions](http://ceres-solver.org/installation.html). The dependencies **Eigen (>=3.3.7)**, **TBB**, **glog (>=0.4.0)** are also used in IC-GVINS. You can install them as follows: ```shell sudo apt install libeigen3-dev libgoogle-glog-dev libtbb-dev ``` If the version cannot be satisfied in your system repository, you should build them from the source code. ### 1.4 OpenCV The supported version is **OpenCV (>=3.2.0)**. You can install OpenCV from your system repository or build from the source code. OpenCV 4 is also supported in IC-GVINS. ``` sudo apt install libopencv-dev ``` ### 1.5 yaml-cpp ```shell sudo apt install libyaml-cpp-dev ``` ## 2 Build and run IC-GVINS ### 2.1 Build the source code ```shell # Make workspace directory mkdir ~/gvins_ws && cd ~/gvins_ws mkdir src && cd src # Clone the repository into src directory git clone https://github.com/i2Nav-WHU/IC-GVINS.git # To gvins_ws directory cd .. # Build the source code using catkin_make # For gcc catkin_make -j8 -DCMAKE_BUILD_TYPE=Release -DCMAKE_C_COMPILER=gcc-8 -DCMAKE_CXX_COMPILER=g++-8 # For clang # catkin_make -j8 -DCMAKE_BUILD_TYPE=Release -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ ``` ### 2.2 Run demo dataset If you have already downloaded the open-sourced datas
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matched fp:1ae23fd8ae6d9a7e, topic:slam, readme:robotics