VINS-Fusion-gpu

This repository is a version of VINS-Fusion with GPU acceleration. It can run on Nvidia TX2 in real-time.

1. Prerequisites

The essential software environment is same as VINS-Fusion. Besides, it requires OpenCV cuda version.(Only test it on OpenCV 3.4.1).

2. Usage

2.1 Change the opencv path in the CMakeLists

In /vins_estimator/CMakeLists.txt, change Line 20 to your path.
In /loop_fusion/CmakeLists.txt, change Line 19 to your path.

2.2 Change the acceleration parameters as you need.

In the config file, there are two parameters for gpu acceleration.
use_gpu: 0 for off, 1 for on
use_gpu_acc_flow: 0 for off, 1 for on
If your GPU resources is limitted or you want to use GPU for other computaion. You can set
use_gpu: 1
use_gpu_acc_flow: 0
If your other application do not require much GPU resources, I recommanded you to set
use_gpu: 1
use_gpu_acc_flow: 1
According to my test, on TX2 if you set this two parameters to 1 at the same time, the GPU usage is about 20%.

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, 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 (12.Jan.2019).

Authors: Tong Qin, Shaozu Cao, Jie Pan, Peiliang Li, and Shaojie Shen from the Aerial Robotics Group, HKUST

Videos:

Related Papers: (papers are not exactly same with code)

• A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors, Tong Qin, Jie Pan, Shaozu Cao, Shaojie Shen, aiXiv pdf

• A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors, Tong Qin, Shaozu Cao, Jie Pan, Shaojie Shen, aiXiv pdf

• 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

• VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator, Tong Qin, Peiliang Li, Shaojie Shen, IEEE Transactions on Robotics pdf

If you use VINS-Fusion for your academic research, please cite our related papers. bib

1. Prerequisites

1.1 Ubuntu and ROS

Ubuntu 64-bit 16.04 or 18.04. ROS Kinetic or Melodic. ROS Installation

1.2. Ceres Solver

Follow Ceres Installation.

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 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 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 to YOUR_DATASET_FOLDER. Take 2011_10_03_drive_0027_synced 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 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 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. Acknowledgements

We use ceres solver for non-linear optimization and DBoW2 for loop detection, a generic camera model and GeographicLib.

8. License

The source code is released under GPLv3 license.

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

For commercial inquiries, please contact Shaojie Shen <eeshaojieATust.hk>.