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:
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A sliding-window optimizer for GNSS/INS integration;
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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;
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Implementation of the marginalization;
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Tools for attitude parameterization and coordinate frames;
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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
We use Ceres Solver to solve the non-linear least squares problem. The supported version is Ceres Solver 2.0.0 or 2.1.0. Please follow Ceres installation instructions.
1.3 Eigen3
The supported version is Eigen 3.3.7~3.3.9.
sudo apt install libeigen3-dev
1.4 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]).