debug topics

/vins_estimator/camera_pose /vins_estimator/camera_pose_visual /vins_estimator/extrinsic /vins_estimator/groundtruth /vins_estimator/image_track /vins_estimator/imu_propagate /vins_estimator/key_poses /vins_estimator/keyframe_point /vins_estimator/keyframe_pose /vins_estimator/margin_cloud /vins_estimator/odometry /vins_estimator/path /vins_estimator/point_cloud /vins_estimator/pure_wheel_propagate /vins_estimator/wheel_preintegration /vins_estimator/wheel_propagate

control topics

restart vins_estimator, rostopic pub /vins_restart std_msgs/Bool true -1
enable/disable imu usage, rostopic pub /vins_imu_switch std_msgs/Bool true -1
enable/disable stereo usage, rostopic pub /vins_cam_switch std_msgs/Bool true -1

use carla data-bag

roslaunch vins vins_rviz.launch # launch rviz
rosrun vins viwo_node config/carla/avp_town04_01.yaml # launch viwo
rosbag play data/avp_town04_20220919.bag -r 0.1 # "-r 0.1" will slow-down publish rate by 0.1

run realsense_stereo_imu_config_ridgeback

package name(defined in package.xml): vins, process name(defined in ridgeback_viwo.launch): viwo_node
source devel/setup.bash
roslaunch vins ridgeback_viwo.launch viwo_node will die, so run rviz and viwo_node seperately
roslaunch vins vins_rviz.launch &
rosrun vins viwo_node config/realsense_d435i/realsense_stereo_imu_config_ridgeback.yaml &
rosbag play data/ridgeback_dark.bag can't play bag in the background
debug topics published in visualization.cpp::registerPub(), as for "/vins_estimator/image_track", FeatureTracker::drawTrack() // red-circle stands for good-tracking feature, blue-circle stands for bad-tracking feature, green-arrow stands for feature movement.

get env ready

docker pull ros:melodic
docker run --gpus all -it -v pwd:pwd -w pwd --ipc=host -v /etc/localtime:/etc/localtime:ro -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=unix$DISPLAY -e GDK_SCALE -e GDK_DPI_SCALE --network host ros:melodic /bin/bash
install ceres solver
install sophus
install other libraries: apt install ros-melodic-image-transport apt install ros-melodic-tf apt install ros-melodic-cv-bridge apt install libopencv-dev

VIW-Fusion

An visual-inertial-wheel fusion odometry

VIW-Fusion is an optimization-based viusla-inertial-wheel fusion odometry, which is developed as a part of my master thesis. First of all, I need to thank HKUST Aerial Robotics Group led by Prof. Shaojie Shen for their outstanding work VINS-Fusion. VIW-Fusion is developed based on VINS-Fusion. Features:

multiple sensors support (stereo cameras+IMU+wheel / mono camera+IMU+wheel)
wheel enhanced visual-inertial initialization
online spatial calibration (transformation between camera, IMU and wheel)
online wheel intrinsic calibration
online temporal calibration (time offset between camera, IMU and wheel)
plane constraint

Performance in the scene with challenge light

We tested Mono VIWO in scenes with drastic changes in light, and the parameters between different scenes remained unchanged. The video is below, if you can't access youtube, please try bilibili: We compare the VIWO with Mono VIO. The trajectory estimated by Mono VIO is completely different from the real trajectory, while the trajectory estimated by Mono VIWO is in good agreement with the real trajectory.

We align the trajectory with the entrance door and exit door to further compare VIWO and Stereo VIO, the trajectory estimated by Mono VIWO is also more reasonable.

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.

Version: 1.14.0

1.3 Sophus

git clone https://github.com/strasdat/Sophus.git
cd Sophus && git checkout a0fe89a323e20c42d3cecb590937eb7a06b8343a
mkdir build && cd ./build
cmake ..
make -j4
sudo make install

2. Build VIW-Fusion

Clone the repository and catkin_make:

cd ~/catkin_ws/src
git clone https://github.com/TouchDeeper/VIW-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. Example

Download dataset here.

roslaunch vins vins_rviz.launch
rosrun vins viwo_node ~/catkin_ws/src/VIW-Fusion/config/realsense_d435i/realsense_stereo_imu_config_ridgeback.yaml 
(optional) rosrun loop_fusion loop_fusion_node ~/catkin_ws/src/VIW-Fusion/config/realsense_d435i/realsense_stereo_imu_config_ridgeback.yaml
rosbag play YOUR_DATASET_FOLDER/ridgeback_dark.bag

4. Citation

If you use this package for your research, a footnote with the link to this repository is appreciated: github.com/TouchDeeper/VIW-Fusion, or for citation with BibTeX:

@misc{ztd2021viwo,
  title={VIW-Fusion: visual-inertial-wheel fusion odometry.},
  author={Tingda Zhuang},
  howpublished={\url{https://github.com/TouchDeeper/VIW-Fusion}},
  year={2021}
}

-------------------- separation line----------------------------

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 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
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. 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 and docker 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 for non-linear optimization and DBoW2 for loop detection, a generic camera model and GeographicLib.

9. 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>.

lygbuaa/VIW-Fusion

debug topics

control topics

use carla data-bag

run realsense_stereo_imu_config_ridgeback

get env ready

VIW-Fusion

An visual-inertial-wheel fusion odometry

Performance in the scene with challenge light

1. Prerequisites

1.1 Ubuntu and ROS

1.2. Ceres Solver

1.3 Sophus

2. Build VIW-Fusion

3. Example

4. Citation

VINS-Fusion

An optimization-based multi-sensor state estimator

1. Prerequisites

1.1 Ubuntu and ROS

1.2. Ceres Solver

2. Build VINS-Fusion

3. EuRoC Example

3.1 Monocualr camera + IMU

3.2 Stereo cameras + IMU

3.3 Stereo cameras

4. KITTI Example

4.1 KITTI Odometry (Stereo)

4.2 KITTI GPS Fusion (Stereo + GPS)

5. VINS-Fusion on car demonstration

6. Run with your devices

6.1 Configuration file

6.2 Camera calibration

7. Docker Support

8. Acknowledgements

9. License