文件列表:

.clang-format (850, 2023-05-09)
LICENSE (35149, 2023-05-09)
config/ (0, 2023-05-09)
config/gvins.yaml (2864, 2023-05-09)
config/visualization.rviz (8621, 2023-05-09)
ic_gvins/ (0, 2023-05-09)
ic_gvins/.clang-format (844, 2023-05-09)
ic_gvins/CMakeLists.txt (2054, 2023-05-09)
ic_gvins/ROS/ (0, 2023-05-09)
ic_gvins/ROS/drawer_rviz.cc (6424, 2023-05-09)
ic_gvins/ROS/drawer_rviz.h (2432, 2023-05-09)
ic_gvins/ROS/fusion_ros.cc (8674, 2023-05-09)
ic_gvins/ROS/fusion_ros.h (1790, 2023-05-09)
ic_gvins/cmake/ (0, 2023-05-09)
ic_gvins/cmake/FindGlog.cmake (15950, 2023-05-09)
ic_gvins/ic_gvins/ (0, 2023-05-09)
ic_gvins/ic_gvins/common/ (0, 2023-05-09)
ic_gvins/ic_gvins/common/angle.h (1730, 2023-05-09)
ic_gvins/ic_gvins/common/earth.h (7299, 2023-05-09)
ic_gvins/ic_gvins/common/gpstime.h (1392, 2023-05-09)
ic_gvins/ic_gvins/common/logging.h (2749, 2023-05-09)
ic_gvins/ic_gvins/common/rotation.h (4214, 2023-05-09)
ic_gvins/ic_gvins/common/timecost.h (2041, 2023-05-09)
ic_gvins/ic_gvins/common/types.h (1422, 2023-05-09)
ic_gvins/ic_gvins/factors/ (0, 2023-05-09)
ic_gvins/ic_gvins/factors/gnss_factor.h (2468, 2023-05-09)
ic_gvins/ic_gvins/factors/marginalization_factor.h (4079, 2023-05-09)
ic_gvins/ic_gvins/factors/marginalization_info.h (11512, 2023-05-09)
ic_gvins/ic_gvins/factors/pose_parameterization.h (2083, 2023-05-09)
ic_gvins/ic_gvins/factors/reprojection_factor.h (6032, 2023-05-09)
ic_gvins/ic_gvins/factors/residual_block_info.h (4272, 2023-05-09)
ic_gvins/ic_gvins/fileio/ (0, 2023-05-09)
ic_gvins/ic_gvins/fileio/filebase.h (1623, 2023-05-09)
ic_gvins/ic_gvins/fileio/fileloader.cc (2802, 2023-05-09)
ic_gvins/ic_gvins/fileio/fileloader.h (1494, 2023-05-09)
ic_gvins/ic_gvins/fileio/filesaver.cc (2098, 2023-05-09)
ic_gvins/ic_gvins/fileio/filesaver.h (1678, 2023-05-09)
ic_gvins/ic_gvins/ic_gvins.cc (69686, 2023-05-09)
... ...

# 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. **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). **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**). ```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 dataset, run the following commands. ```shell # 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](http://docs.ros.org/en/api/sensor_msgs/html/msg/Image.html) | /cam0 | 10 | 20 | | IMU | [sensor_msgs/Imu](http://docs.ros.org/en/api/sensor_msgs/html/msg/Imu.html) | /imu0 | 100 | 200 | | GNSS-RTK | [sensor_msgs/NavSatFix](http://docs.ros.org/en/api/sensor_msgs/html/msg/NavSatFix.html) | /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](https://pan.baidu.com/s/1CJj0Z1vClU4aL8zSna-LzQ) (gyvr) | | urban39 (bottom) | 1856 | 10678 | [urban39.bag](https://pan.baidu.com/s/14CHl7LaIIkBKpwhyuPPbPA) (mnrn) | ### 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](https://pan.baidu.com/s/18yRYUQdu_-DmrYnXQy9VNQ) (igks) | | building (bottom) | 1820 | 2560 | [building.bag](https://pan.baidu.com/s/1Y48jFmdAOBF4y30KBK9bAw) (2drg) | ### 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](https://github.com/MichaelGrupp/evo) to evaluate the TUM trajectory files. We also provide some useful scripts ([evaluate_odometry](https://github.com/i2Nav-WHU/evaluate_odometry)) for evaluation. ## 4 Acknowledgements We thanks the following projects for the helps in developing and evaluating the IC-GVINS: - [OB_GINS](https://github.com/i2Nav-WHU/OB_GINS): An Optimization-Based GNSS/INS Integrated Navigation System - [VINS-Fusion](https://github.com/HKUST-Aerial-Robotics/VINS-Fusion): An optimization-based multi-sensor state estimator - [Complex Urban Dataset](https://sites.google.com/view/complex-urban-dataset): Complex Urban Dataset with Multi-level Sensors from Highly Diverse Urban Environments - [evo](https://github.com/MichaelGrupp/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](mailto:thl@whu.edu.cn)) or open an issue at this repository. For commercial usage, please contact Prof. Xiaoji Niu ([xjniu@whu.edu.cn](mailto:xjniu@whu.edu.cn)).

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