VINS-Fusion.zip

# VINS-Fusion ## An optimization-based multi-sensor state estimator <img src="https://github.com/HKUST-Aerial-Robotics/VINS-Fusion/blob/master/support_files/image/vins_logo.png" width = 55% height = 55% div align=left /> <img src="https://github.com/HKUST-Aerial-Robotics/VINS-Fusion/blob/master/support_files/image/kitti.png" width = 34% height = 34% div align=center /> 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 <img src="https://github.com/HKUST-Aerial-Robotics/VINS-Fusion/blob/master/support_files/image/kitti_rank.png" width = 80% height = 80% /> 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:** <a href="https://www.youtube.com/embed/1qye82aW7nI" target="_blank"><img src="http://img.youtube.com/vi/1qye82aW7nI/0.jpg" alt="VINS" width="320" height="240" border="10" /></a> **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 ``` <img src="https://github.com/HKUST-Aerial-Robotics/VINS-Fusion/blob/master/support_files/image/euroc.gif" width = 430 height = 240 /> ## 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 ``` <img src="https://github.com/HKUST-Aerial-Robotics/VINS-Fusion/blob/master/support_files/image/kitti.gif" width = 430 height = 240 /> ## 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 ``` <img src="https://github.com/HKUST-Aerial-Robotics/VINS-Fusion/blob/master/support_files/image/car_gif.gif" width = 430 height = 240 /> ## 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 envi