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IC-GVINS

A Robust, Real-time, INS-Centric GNSS-Visual-Inertial Navigation System

IC-GVINS is a robust, real-time, inertial navigation system (INS)-Centric GNSS-Visual-Inertial navigation system, in which the precise INS is fully utilized in both the state estimation and visual processes. To improve the system robustness, the INS information is employed during the whole keyframe-based visual process, with strict outlier-culling strategy. The GNSS is adopted to perform an accurate and convenient initialization, and is further employed to achieve absolute positioning in large-scale environments. The IMU, visual, and GNSS measurements are tightly fused within the framework of factor graph optimization.

overview

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, “IC-GVINS: A Robust, Real-time, INS-Centric GNSS-Visual-Inertial Navigation System for Wheeled Robot,” Apr. 2022. [Online]. Available: https://arxiv.org/abs/2204.04962v1
  • 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. cover

Contacts:

  • For any technique problem, you can send an email to Dr. Hailiang Tang (thl@whu.edu.cn).
  • 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).

# 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 and ROS Noetic installation instructions for Ubuntu 20.04.

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.

The dependencies Eigen (>=3.3.7) and glog (>=0.4.0) are also used in IC-GVINS. You can install them as follows:

sudo apt install libeigen3-dev libgoogle-glog-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

sudo apt install libyaml-cpp-dev

2 Build and run IC-GVINS

2.1 Build the source code

# 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 dataset, run the following commands.

# Open a terminal and source the workspace environments
# For bash
source ~/gvins_ws/devel/setup.bash
# For zsh
# source ~/gvins_ws/devel/setup.zsh

# Run IC-GVINS node
# You should change the path in both the configuration file and command line
roslaunch ic_gvins ic_gvins.launch configfile:=path/urban38/IC-GVINS/gvins.yaml

# Open another terminal to play the ROS bag
rosbag play path/urban38/urban38.bag

3 Datasets

3.1 Format

We use standard ROS bag for IC-GVINS. The employed messages are as follows:

Sensor Message Default Topic KAIST Dataset (Hz) IC-GVINS Dataset (Hz)
Camera sensor_msgs/Image /cam0 10 20
IMU sensor_msgs/Imu /imu0 100 200
GNSS-RTK sensor_msgs/NavSatFix /gnss0 1 1

The IMU should be in front-right-down format in the IC-GVINS.

3.2 KAIST Complex Urban Dataset

The tested sequences are urban38 and urban39.

Sequence Time length (seconds) Trajectory Length (m) Baidu Cloud Link
urban38 (top) 2154 11191 urban38.bag (gyvr)
urban39 (bottom) 1856 10678 urban39.bag (mnrn)

urban38

urban39

3.3 IC-GVINS Robot Dataset

We also open source our self-collected robot dataset.

Sequence Time length (seconds) Trajectory Length (m) Baidu Cloud Link
campus (top) 950 1337 campus.bag (igks)
building (bottom) 1820 2560 building.bag (2drg)

campus

building

3.4 Your own dataset

You can run IC-GVINS with your self-collected dataset. Keep in mind the following notes:

  1. You should prepare well-synchronized GNSS, Camera, and IMU data in a ROS bag;
  2. The IMU data should be in front-right-down format;
  3. Modify the topic names in the ic_gvins.launch file;
  4. Modify the parameters in the configuration file.

3.5 Evaluation

We use evo to evaluate the TUM trajectory files. We also provide some useful scripts (evaluate_odometry) for evaluation.

4 Acknowledgements

We thanks the following projects for the helps in developing and evaluating the IC-GVINS:

  • OB_GINS: An Optimization-Based GNSS/INS Integrated Navigation System
  • VINS-Fusion: An optimization-based multi-sensor state estimator
  • Complex Urban Dataset: Complex Urban Dataset with Multi-level Sensors from Highly Diverse Urban Environments
  • evo: Python package for the evaluation of odometry and SLAM

5 License

The source code is released under GPLv3 license.

We are still working on improving the code. For any technical issues, please contact Dr. Hailiang Tang (thl@whu.edu.cn) or open an issue at this repository.

For commercial usage, please contact Prof. Xiaoji Niu (xjniu@whu.edu.cn).