An open source platform for visual-inertial navigation research.
Welcome to the OpenVINS project! The OpenVINS project houses some core computer vision code along with a state-of-the art filter-based visual-inertial estimator. The core filter is an Extended Kalman filter which fuses inertial information with sparse visual feature tracks. These visual feature tracks are fused leveraging the Multi-State Constraint Kalman Filter (MSCKF) sliding window formulation which allows for 3D features to update the state estimate without directly estimating the feature states in the filter. Inspired by graph-based optimization systems, the included filter has modularity allowing for convenient covariance management with a proper type-based state system. Please take a look at the feature list below for full details on what the system supports.
ov_plane - A real-time monocular visual-inertial odometry (VIO) system which leverages environmental planes. At the core it presents an efficient robust monocular-based plane detection algorithm which does not require additional sensing modalities such as a stereo, depth camera or neural network. The plane detection and tracking algorithm enables real-time regularization of point features to environmental planes which are either maintained in the state vector as long-lived planes, or marginalized for efficiency. Planar regularities are applied to both in-state SLAM and out-of-state MSCKF point features, enabling long-term point-to-plane loop-closures due to the large spacial volume of planes.
vicon2gt - This utility was created to generate groundtruth trajectories using a motion capture system (e.g. Vicon or OptiTrack) for use in evaluating visual-inertial estimation systems. Specifically we calculate the inertial IMU state (full 15 dof) at camera frequency rate and generate a groundtruth trajectory similar to those provided by the EurocMav datasets. Performs fusion of inertial and motion capture information and estimates all unknown spacial-temporal calibrations between the two sensors.
ov_maplab - This codebase contains the interface wrapper for exporting visual-inertial runs from OpenVINS into the ViMap structure taken by maplab. The state estimates and raw images are appended to the ViMap as OpenVINS runs through a dataset. After completion of the dataset, features are re-extract and triangulate with maplab's feature system. This can be used to merge multi-session maps, or to perform a batch optimization after first running the data through OpenVINS. Some example have been provided along with a helper script to export trajectories into the standard groundtruth format.
ov_secondary - This is an example secondary thread which provides loop closure in a loosely coupled manner for OpenVINS. This is a modification of the code originally developed by the HKUST aerial robotics group and can be found in their VINS-Fusion repository. Here we stress that this is a loosely coupled method, thus no information is returned to the estimator to improve the underlying OpenVINS odometry. This codebase has been modified in a few key areas including: exposing more loop closure parameters, subscribing to camera intrinsics, simplifying configuration such that only topics need to be supplied, and some tweaks to the loop closure detection to improve frequency.
This code was written by the Robot Perception and Navigation Group (RPNG) at the University of Delaware. If you have any issues with the code please open an issue on our github page with relevant implementation details and references. For researchers that have leveraged or compared to this work, please cite the following:
@Conference{Geneva2020ICRA,
Title = {{OpenVINS}: A Research Platform for Visual-Inertial Estimation},
Author = {Patrick Geneva and Kevin Eckenhoff and Woosik Lee and Yulin Yang and Guoquan Huang},
Booktitle = {Proc. of the IEEE International Conference on Robotics and Automation},
Year = {2020},
Address = {Paris, France},
Url = {\url{https://github.com/rpng/open_vins}}
}
The codebase and documentation is licensed under the GNU General Public License v3 (GPL-3). You must preserve the copyright and license notices in your derivative work and make available the complete source code with modifications under the same license (see this; this is not legal advice).