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https://github.com/rpng/r-vio
Robocentric Visual-Inertial Odometry
https://github.com/rpng/r-vio
ijrr iros kalman-filtering localization msckf robocentric vins vio visual-inertial-odometry
Last synced: 3 days ago
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Robocentric Visual-Inertial Odometry
- Host: GitHub
- URL: https://github.com/rpng/r-vio
- Owner: rpng
- License: gpl-3.0
- Created: 2019-02-11T19:21:14.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-04-23T19:05:30.000Z (over 1 year ago)
- Last Synced: 2024-12-27T15:09:12.584Z (10 days ago)
- Topics: ijrr, iros, kalman-filtering, localization, msckf, robocentric, vins, vio, visual-inertial-odometry
- Language: C++
- Homepage: https://journals.sagepub.com/doi/10.1177/0278364919853361
- Size: 71.6 MB
- Stars: 767
- Watchers: 37
- Forks: 174
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# R-VIO
R-VIO is an efficient, lightweight, **robocentric** visual-inertial navigation algorithm for consistent 3D motion tracking using only a monocular camera and a single IMU. Different from the standard world-centric algorithms which directly estimate absolute motion of the mobile platform with respect to a fixed, gravity-aligned, global frame of reference, R-VIO i) estimates relative motion of higher accuracy with respect to a moving, local frame (the IMU frame here), and ii) incrementally updates global pose (orientation and position) through a composition step. This code implements our robocentric sliding-window filtering-based VIO formulation that was originally proposed in our *IROS2018* paper and presented in detail in our recent *IJRR* paper:
- Zheng Huai and Guoquan Huang, **Robocentric visual-inertial odometry**, *The International Journal of Robotics Research (IJRR)*, 2022: [download](https://journals.sagepub.com/doi/10.1177/0278364919853361).
```
@article{huai2022robocentric,
title={Robocentric visual-inertial odometry},
author={Huai, Zheng and Huang, Guoquan},
journal={The International Journal of Robotics Research},
volume={41},
number={7},
pages={667--689},
year={2022},
publisher={SAGE Publications Sage UK: London, England}
}
```
- Zheng Huai and Guoquan Huang, **Robocentric visual-inertial odometry**, *IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)*, Madrid, Spain, Oct 1-5, 2018: [download](https://ieeexplore.ieee.org/document/8593643).
```
@inproceedings{huai2018robocentric,
title = {Robocentric visual-inertial odometry},
author = {Huai, Zheng and Huang, Guoquan},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages = {6319--6326},
year = {2018},
address = {Madrid, Spain}
}
```
This work has been further extended in our *IEEE RA-L* paper below, and the proposed [R-VIO2](https://github.com/rpng/R-VIO2) is also open sourced.
- Zheng Huai and Guoquan Huang, **Square-Root Robocentric Visual-Inertial Odometry with Online Spatiotemporal Calibration**, *IEEE Robotics and Automation Letters (RA-L)*, 2022: [download](https://ieeexplore.ieee.org/document/9830847).
```
@article{huai2022square,
title={Square-root robocentric visual-inertial odometry with online spatiotemporal calibration},
author={Huai, Zheng and Huang, Guoquan},
journal={IEEE Robotics and Automation Letters},
volume={7},
number={4},
pages={9961--9968},
year={2022},
publisher={IEEE}
}
```
IROS video (**EuRoC MAV** dataset): [YouTube](https://www.youtube.com/watch?v=UtiZ0EKa55M).
IJRR video (9.8km **Urban Driving** test): [YouTube](https://www.youtube.com/watch?v=l9IC2ddBEYQ).
## 1. Prerequisites
We have tested this code under Ubuntu **16.04** and ROS **Kinetic**.
### ROS
Download and install instructions can be found at: http://wiki.ros.org/kinetic/Installation/Ubuntu.
Additional ROS packages needed: tf, sensor_msgs, geometry_msgs, nav_msgs, cv_bridge, eigen_conversions.
### Eigen
Download and install instructions can be found at: http://eigen.tuxfamily.org. **Required at least 3.1.0**.
### OpenCV
Download and install instructions can be found at: http://opencv.org. **Required at leat 2.4.3**. **Tested with 2.4.11 and 3.3.1**.
## 2. Build and Run
First, `git clone` the repository and `catkin_make` it. Then, to run `rvio` with single camera/IMU inputs from the ROS topics `/camera/image_raw` and `/imu`, a config file in *config* folder and the corresponding launch file in *launch* folder (for example, `rvio_euroc.yaml` and `euroc.launch` are for [EuRoC](https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets) dataset) are needed, and to visualize the outputs of R-VIO please use `rviz` with the settings file `rvio_rviz.rviz` in *config* folder.
```
Terminal 1: roscore
```
```
Terminal 2: rviz (AND OPEN rvio_rviz.rviz IN THE CONFIG FOLDER)
```
```
Terminal 3: roslaunch rvio euroc.launch
```
```
Terminal 4: rosbag play --pause V1_01_easy.bag /cam0/image_raw:=/camera/image_raw /imu0:=/imu
```
Note that when testing the `Machine Hall` sequences, you should skip the data in the first few seconds (e.g., 40s for `MH_01_easy`) which are used for initializing the map for SLAM-based algorithms.
You can also run R-VIO with your own sensors (data) by creating a config file `rvio_NAME_OF_YOUR_DATA.yaml` in *config* folder and the corresponding launch file `NAME_OF_YOUR_DATA.launch` in *launch* folder, referring to our EuRoC example.
## 3. License
This code is released under [GNU General Public License v3 (GPL-3.0)](https://www.gnu.org/licenses/gpl-3.0.en.html).