R-VIO

R-VIO is an efficient, lightweight, robocentric visual-inertial odometry algorithm for consistent 3D motion tracking using only a monocular camera and a 6-axis IMU. Different from standard world-centric VINS algorithms which directly estimate absolute motion of the sensing platform with respect to a fixed, gravity-aligned global frame of reference {G}, R-VIO estimates the relative motion of high accuracy with respect to a moving local frame {R} (for example, IMU frame) and updates the global pose (orientation and position) estimate through composition. This algorithm is developed with the robocentric sliding-window filtering-based VIO framework that we originally proposed in our IROS 2018 paper and further extended in our recent IJRR submission:

• Zheng Huai and Guoquan Huang, Robocentric visual-inertial odometry, The International Journal of Robotics Research, Nov 2018. [cond. accepted]

• Zheng Huai and Guoquan Huang, Robocentric visual-inertial odometry, International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, Oct 1-5, 2018.

• Zheng Huai and Guoquan Huang, Robocentric visual-inertial odometry, arXiv:1805.04031, May 2018: https://arxiv.org/abs/1805.04031

• For the implementation details, please also refer to our companion technical report: http://udel.edu/~ghuang/papers/tr_rvio_ijrr.pdf.

1. Prerequisites

We have tested the 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 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

You can also run R-VIO with your own sensor (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 the above EuRoC example.

3. License

The source code is released under GPLv3 license.

We are still working on improving the code reliability. For any technical issue, please contact Zheng Huai: zhuai@udel.edu.

For commercial inquiries, please contact Guoquan (Paul) Huang: ghuang@udel.edu.