文件列表:

CMakeLists.txt (2387, 2023-04-23)
LICENSE (35149, 2023-04-23)
config/ (0, 2023-04-23)
config/rvio_euroc.yaml (3093, 2023-04-23)
config/rvio_rviz.rviz (7767, 2023-04-23)
launch/ (0, 2023-04-23)
launch/euroc.launch (273, 2023-04-23)
package.xml (620, 2023-04-23)
rvio.gif (28748750, 2023-04-23)
src/ (0, 2023-04-23)
src/rvio/ (0, 2023-04-23)
src/rvio/FeatureDetector.cc (4815, 2023-04-23)
src/rvio/FeatureDetector.h (2381, 2023-04-23)
src/rvio/InputBuffer.cc (2259, 2023-04-23)
src/rvio/InputBuffer.h (1782, 2023-04-23)
src/rvio/PreIntegrator.cc (5773, 2023-04-23)
src/rvio/PreIntegrator.h (1518, 2023-04-23)
src/rvio/Ransac.cc (8144, 2023-04-23)
src/rvio/Ransac.h (3964, 2023-04-23)
src/rvio/System.cc (13862, 2023-04-23)
src/rvio/System.h (2330, 2023-04-23)
src/rvio/Tracker.cc (12515, 2023-04-23)
src/rvio/Tracker.h (3287, 2023-04-23)
src/rvio/Updater.cc (20408, 2023-04-23)
src/rvio/Updater.h (1695, 2023-04-23)
src/rvio_mono.cc (3515, 2023-04-23)
src/util/ (0, 2023-04-23)
src/util/Numerics.h (10046, 2023-04-23)

# 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).

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