OB_GINS

Optimization-Based GNSS/INS Integrated Navigation System

We open-source OB_GINS, an optimization-based GNSS/INS integrated navigation system. The main features of OB_GINS are as follows:

• A sliding-window optimizer for GNSS/INS integration;

• Abstract IMU-preintegration implementation, including:

  • The normal IMU preintegration without the Earth's rotation consideration;
  • The normal IMU/ODO preintegration;
  • The refined IMU preintegration with the Earth's rotation consideration;
  • The refined IMU/ODO preintegration;

• Implementation of the marginalization;

• Tools for attitude parameterization and coordinate frames;

• Tools for file IO;

Authors: Hailiang Tang, Xiaoji Niu, and Tisheng Zhang from the Integrated and Intelligent Navigation (i2Nav) Group, Wuhan University.

Related Paper:

• 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.
• Le Chang, Xiaoji Niu, and Tianyi Liu, “GNSS/IMU/ODO/LiDAR-SLAM Integrated Navigation System Using IMU/ODO Pre-Integration,” Sensors, vol. 20, no. 17, p. 4702, Aug. 2020, doi: 10.3390/s20174702.
• Junxiang Jiang, Xiaoji Niu, and Jingnan Liu, “Improved IMU Preintegration with Gravity Change and Earth Rotation for Optimization-Based GNSS/VINS,” Remote Sensing, vol. 12, no. 18, p. 3048, Sep. 2020, doi: 10.3390/rs12183048.

If you use this software for your academic research, please cite our related papers; and give acknowledgement like:

English version: “The authors would like to acknowledge Dr. Xiaoji Niu and the Integrated and Intelligent Navigation (i2Nav) group from Wuhan University for providing the OB_GINS software that was used in the paper.”
中文模板：“本文作者感谢武汉大学多源智能导航实验室和牛小骥教授开源的OB_GINS软件平台。”

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).

# Ubuntu 18.04

# gcc-8
sudo apt install gcc-8 g++-8

# or clang
# sudo apt install clang

1.2 Ceres

Follow Ceres installation instructions.

1.3 abseil-cpp

Follow abseil-cpp installation instructions.

1.4 Eigen3

sudo apt install libeigen3-dev

1.5 yaml-cpp

sudo apt install libyaml-cpp-dev

2 Build OB_GINS and run demo

Once the prerequisites have been installed, you can clone this repository and build OB_GINS as follows:

# Clone the repository
git clone https://github.com/i2Nav-WHU/OB_GINS.git ~/

# Build OB_GINS
cd ~/OB_GINS
mkdir build && cd build

# gcc
cmake ../ -DCMAKE_BUILD_TYPE=Release -DCMAKE_C_COMPILER=gcc-8 -DCMAKE_CXX_COMPILER=g++-8
# clang
# cmake ../ -DCMAKE_BUILD_TYPE=Release -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++

make -j8

# Run demo dataset
cd ~/OB_GINS
./bin/ob_gins ./dataset/ob_gins.yaml

# Wait until the program finish

3 Datasets

3.1 Demo dataset

We offer a demo dataset with configuration file, which are located at dataset directory.

3.2 awesome-gins-datasets

One can find our open-source datasets at awesome-gins-datasets.

3.3 Your own dataset

The data formats used in OB_GINS are the same as the formats defined at awesome-gins-datasets. You can follow the formats to prepare your own datasets, or you can modify the source code as you need.

4 Acknowledgements

We thanks VINS-Fusion for providing a excellent platform for SLAM learners.

5 License

The source code is released under GPLv3 license.

We are still working on improving the code reliability. For any technical issues, please contact Hailiang Tang ([email protected]) or open an issue at this repository.

For commercial usage, please contact Prof. Xiaoji Niu ([email protected]).