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https://github.com/HKUST-Aerial-Robotics/VINS-Fusion

An optimization-based multi-sensor state estimator
https://github.com/HKUST-Aerial-Robotics/VINS-Fusion

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An optimization-based multi-sensor state estimator

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# VINS-Fusion

## An optimization-based multi-sensor state estimator








VINS-Fusion is an optimization-based multi-sensor state estimator, which achieves accurate self-localization for autonomous applications (drones, cars, and AR/VR). VINS-Fusion is an extension of [VINS-Mono](https://github.com/HKUST-Aerial-Robotics/VINS-Mono), which supports multiple visual-inertial sensor types (mono camera + IMU, stereo cameras + IMU, even stereo cameras only). We also show a toy example of fusing VINS with GPS. 

**Features:**

- multiple sensors support (stereo cameras / mono camera+IMU / stereo cameras+IMU)

- online spatial calibration (transformation between camera and IMU)

- online temporal calibration (time offset between camera and IMU)

- visual loop closure






We are the **top** open-sourced stereo algorithm on [KITTI Odometry Benchmark](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) (12.Jan.2019).



**Authors:** [Tong Qin](http://www.qintonguav.com), Shaozu Cao, Jie Pan, [Peiliang Li](https://peiliangli.github.io/), and [Shaojie Shen](http://www.ece.ust.hk/ece.php/profile/facultydetail/eeshaojie) from the [Aerial Robotics Group](http://uav.ust.hk/), [HKUST](https://www.ust.hk/)



**Videos:**



VINS



**Related Paper:** (paper is not exactly same with code)



* **Online Temporal Calibration for Monocular Visual-Inertial Systems**, Tong Qin, Shaojie Shen, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS, 2018), **best student paper award** [pdf](https://ieeexplore.ieee.org/abstract/document/8593603)



* **VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator**, Tong Qin, Peiliang Li, Shaojie Shen, IEEE Transactions on Robotics [pdf](https://ieeexplore.ieee.org/document/8421746/?arnumber=8421746&source=authoralert) 



*If you use VINS-Fusion for your academic research, please cite our related papers. [bib](https://github.com/HKUST-Aerial-Robotics/VINS-Fusion/blob/master/support_files/paper_bib.txt)*



## 1. Prerequisites

### 1.1 **Ubuntu** and **ROS**

Ubuntu 64-bit 16.04 or 18.04.

ROS Kinetic or Melodic. [ROS Installation](http://wiki.ros.org/ROS/Installation)



### 1.2. **Ceres Solver**

Follow [Ceres Installation](http://ceres-solver.org/installation.html).



## 2. Build VINS-Fusion

Clone the repository and catkin_make:

```

    cd ~/catkin_ws/src

    git clone https://github.com/HKUST-Aerial-Robotics/VINS-Fusion.git

    cd ../

    catkin_make

    source ~/catkin_ws/devel/setup.bash

```

(if you fail in this step, try to find another computer with clean system or reinstall Ubuntu and ROS)



## 3. EuRoC Example

Download [EuRoC MAV Dataset](http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets) to YOUR_DATASET_FOLDER. Take MH_01 for example, you can run VINS-Fusion with three sensor types (monocular camera + IMU, stereo cameras + IMU and stereo cameras). 

Open four terminals, run vins odometry, visual loop closure(optional), rviz and play the bag file respectively. 

Green path is VIO odometry; red path is odometry under visual loop closure.



### 3.1 Monocualr camera + IMU



```

    roslaunch vins vins_rviz.launch

    rosrun vins vins_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_mono_imu_config.yaml 

    (optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_mono_imu_config.yaml 

    rosbag play YOUR_DATASET_FOLDER/MH_01_easy.bag

```



### 3.2 Stereo cameras + IMU



```

    roslaunch vins vins_rviz.launch

    rosrun vins vins_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_stereo_imu_config.yaml 

    (optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_stereo_imu_config.yaml 

    rosbag play YOUR_DATASET_FOLDER/MH_01_easy.bag

```



### 3.3 Stereo cameras



```

    roslaunch vins vins_rviz.launch

    rosrun vins vins_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_stereo_config.yaml 

    (optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_stereo_config.yaml 

    rosbag play YOUR_DATASET_FOLDER/MH_01_easy.bag

```






## 4. KITTI Example

### 4.1 KITTI Odometry (Stereo)

Download [KITTI Odometry dataset](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) to YOUR_DATASET_FOLDER. Take sequences 00 for example,

Open two terminals, run vins and rviz respectively. 

(We evaluated odometry on KITTI benchmark without loop closure funtion)

```

    roslaunch vins vins_rviz.launch

    (optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VINS-Fusion/config/kitti_odom/kitti_config00-02.yaml

    rosrun vins kitti_odom_test ~/catkin_ws/src/VINS-Fusion/config/kitti_odom/kitti_config00-02.yaml YOUR_DATASET_FOLDER/sequences/00/ 

```

### 4.2 KITTI GPS Fusion (Stereo + GPS)

Download [KITTI raw dataset](http://www.cvlibs.net/datasets/kitti/raw_data.php) to YOUR_DATASET_FOLDER. Take [2011_10_03_drive_0027_synced](https://s3.eu-central-1.amazonaws.com/avg-kitti/raw_data/2011_10_03_drive_0027/2011_10_03_drive_0027_sync.zip) for example.

Open three terminals, run vins, global fusion and rviz respectively. 

Green path is VIO odometry; blue path is odometry under GPS global fusion.

```

    roslaunch vins vins_rviz.launch

    rosrun vins kitti_gps_test ~/catkin_ws/src/VINS-Fusion/config/kitti_raw/kitti_10_03_config.yaml YOUR_DATASET_FOLDER/2011_10_03_drive_0027_sync/ 

    rosrun global_fusion global_fusion_node

```






## 5. VINS-Fusion on car demonstration

Download [car bag](https://drive.google.com/open?id=10t9H1u8pMGDOI6Q2w2uezEq5Ib-Z8tLz) to YOUR_DATASET_FOLDER.

Open four terminals, run vins odometry, visual loop closure(optional), rviz and play the bag file respectively. 

Green path is VIO odometry; red path is odometry under visual loop closure.

```

    roslaunch vins vins_rviz.launch

    rosrun vins vins_node ~/catkin_ws/src/VINS-Fusion/config/vi_car/vi_car.yaml 

    (optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VINS-Fusion/config/vi_car/vi_car.yaml 

    rosbag play YOUR_DATASET_FOLDER/car.bag

```






## 6. Run with your devices 

VIO is not only a software algorithm, it heavily relies on hardware quality. For beginners, we recommend you to run VIO with professional equipment, which contains global shutter cameras and hardware synchronization.



### 6.1 Configuration file

Write a config file for your device. You can take config files of EuRoC and KITTI as the example. 



### 6.2 Camera calibration

VINS-Fusion support several camera models (pinhole, mei, equidistant). You can use [camera model](https://github.com/hengli/camodocal) to calibrate your cameras. We put some example data under /camera_models/calibrationdata to tell you how to calibrate.

```

cd ~/catkin_ws/src/VINS-Fusion/camera_models/camera_calib_example/

rosrun camera_models Calibrations -w 12 -h 8 -s 80 -i calibrationdata --camera-model pinhole

```



## 7. Docker Support

To further facilitate the building process, we add docker in our code. Docker environment is like a sandbox, thus makes our code environment-independent. To run with docker, first make sure [ros](http://wiki.ros.org/ROS/Installation) and [docker](https://docs.docker.com/install/linux/docker-ce/ubuntu/) are installed on your machine. Then add your account to `docker` group by `sudo usermod -aG docker $YOUR_USER_NAME`. **Relaunch the terminal or logout and re-login if you get `Permission denied` error**, type:

```

cd ~/catkin_ws/src/VINS-Fusion/docker

make build

```

Note that the docker building process may take a while depends on your network and machine. After VINS-Fusion successfully built, you can run vins estimator with script `run.sh`.

Script `run.sh` can take several flags and arguments. Flag `-k` means KITTI, `-l` represents loop fusion, and `-g` stands for global fusion. You can get the usage details by `./run.sh -h`. Here are some examples with this script:

```

# Euroc Monocualr camera + IMU

./run.sh ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_mono_imu_config.yaml



# Euroc Stereo cameras + IMU with loop fusion

./run.sh -l ~/catkin_ws/src/VINS-Fusion/config/euroc/euroc_mono_imu_config.yaml



# KITTI Odometry (Stereo)

./run.sh -k ~/catkin_ws/src/VINS-Fusion/config/kitti_odom/kitti_config00-02.yaml YOUR_DATASET_FOLDER/sequences/00/



# KITTI Odometry (Stereo) with loop fusion

./run.sh -kl ~/catkin_ws/src/VINS-Fusion/config/kitti_odom/kitti_config00-02.yaml YOUR_DATASET_FOLDER/sequences/00/



#  KITTI GPS Fusion (Stereo + GPS)

./run.sh -kg ~/catkin_ws/src/VINS-Fusion/config/kitti_raw/kitti_10_03_config.yaml YOUR_DATASET_FOLDER/2011_10_03_drive_0027_sync/



```

In Euroc cases, you need open another terminal and play your bag file. If you need modify the code, simply re-run `./run.sh` with proper auguments after your changes.



## 8. Acknowledgements

We use [ceres solver](http://ceres-solver.org/) for non-linear optimization and [DBoW2](https://github.com/dorian3d/DBoW2) for loop detection, a generic [camera model](https://github.com/hengli/camodocal) and [GeographicLib](https://geographiclib.sourceforge.io/).



## 9. License

The source code is released under [GPLv3](http://www.gnu.org/licenses/) license.



We are still working on improving the code reliability. For any technical issues, please contact Tong Qin .



For commercial inquiries, please contact Shaojie Shen .