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https://github.com/rpng/open_vins

An open source platform for visual-inertial navigation research.
https://github.com/rpng/open_vins

ekf-localization msckf open-vins research-platform robotics sensor-calibration slam vio visual-inertial-odometry

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An open source platform for visual-inertial navigation research.

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README 

        
# OpenVINS



[![ROS 1 Workflow](https://github.com/rpng/open_vins/actions/workflows/build_ros1.yml/badge.svg)](https://github.com/rpng/open_vins/actions/workflows/build_ros1.yml)

[![ROS 2 Workflow](https://github.com/rpng/open_vins/actions/workflows/build_ros2.yml/badge.svg)](https://github.com/rpng/open_vins/actions/workflows/build_ros2.yml)

[![ROS Free Workflow](https://github.com/rpng/open_vins/actions/workflows/build.yml/badge.svg)](https://github.com/rpng/open_vins/actions/workflows/build.yml)



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](https://en.wikipedia.org/wiki/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)](https://ieeexplore.ieee.org/document/4209642) 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.



* Github project page - https://github.com/rpng/open_vins

* Documentation - https://docs.openvins.com/

* Getting started guide - https://docs.openvins.com/getting-started.html

* Publication reference - https://pgeneva.com/downloads/papers/Geneva2020ICRA.pdf



## News / Events



* **May 11, 2023** - Inertial intrinsic support released as part of v2.7 along with a few bug fixes and improvements to stereo KLT tracking. Please check out the [release page](https://github.com/rpng/open_vins/releases/tag/v2.7) for details.

* **April 15, 2023** - Minor update to v2.6.3 to support incremental feature triangulation of active features for downstream applications, faster zero-velocity update, small bug fixes, some example realsense configurations, and cached fast state prediction. Please check out the [release page](https://github.com/rpng/open_vins/releases/tag/v2.6.3) for details.

* **April 3, 2023** - We have released a monocular plane-aided VINS, termed [ov_plane](https://github.com/rpng/ov_plane), which leverages the OpenVINS project. Both now support the released [Indoor AR Table](https://github.com/rpng/ar_table_dataset) dataset.

* **July 14, 2022** - Improved feature extraction logic for >100hz tracking, some bug fixes and updated scripts. See v2.6.1 [PR#259](https://github.com/rpng/open_vins/pull/259) and v2.6.2 [PR#264](https://github.com/rpng/open_vins/pull/264).

* **March 14, 2022** - Initial dynamic initialization open sourcing, asynchronous subscription to inertial readings and publishing of odometry, support for lower frequency feature tracking. See v2.6 [PR#232](https://github.com/rpng/open_vins/pull/232) for details.

* **December 13, 2021** - New YAML configuration system, ROS2 support, Docker images, robust static initialization based on disparity, internal logging system to reduce verbosity, image transport publishers, dynamic number of features support, and other small fixes. See

  v2.5 [PR#209](https://github.com/rpng/open_vins/pull/209) for details.

* **July 19, 2021** - Camera classes, masking support, alignment utility, and other small fixes. See

  v2.4 [PR#117](https://github.com/rpng/open_vins/pull/186) for details.

* **December 1, 2020** - Released improved memory management, active feature pointcloud publishing, limiting number of

  features in update to bound compute, and other small fixes. See

  v2.3 [PR#117](https://github.com/rpng/open_vins/pull/117) for details.

* **November 18, 2020** - Released groundtruth generation utility package, [vicon2gt](https://github.com/rpng/vicon2gt)

  to enable creation of groundtruth trajectories in a motion capture room for evaulating VIO methods.

* **July 7, 2020** - Released zero velocity update for vehicle applications and direct initialization when standing

  still. See [PR#79](https://github.com/rpng/open_vins/pull/79) for details.

* **May 18, 2020** - Released secondary pose graph example

  repository [ov_secondary](https://github.com/rpng/ov_secondary) based

  on [VINS-Fusion](https://github.com/HKUST-Aerial-Robotics/VINS-Fusion). OpenVINS now publishes marginalized feature

  track, feature 3d position, and first camera intrinsics and extrinsics.

  See [PR#66](https://github.com/rpng/open_vins/pull/66) for details and discussion.

* **April 3, 2020** - Released [v2.0](https://github.com/rpng/open_vins/releases/tag/v2.0) update to the codebase with

  some key refactoring, ros-free building, improved dataset support, and single inverse depth feature representation.

  Please check out the [release page](https://github.com/rpng/open_vins/releases/tag/v2.0) for details.

* **January 21, 2020** - Our paper has been accepted for presentation in [ICRA 2020](https://www.icra2020.org/). We look

  forward to seeing everybody there! We have also added links to a few videos of the system running on different

  datasets.

* **October 23, 2019** - OpenVINS placed first in the [IROS 2019 FPV Drone Racing VIO Competition

  ](http://rpg.ifi.uzh.ch/uzh-fpv.html). We will be giving a short presentation at

  the [workshop](https://wp.nyu.edu/workshopiros2019mav/) at 12:45pm in Macau on November 8th.

* **October 1, 2019** - We will be presenting at the [Visual-Inertial Navigation: Challenges and Applications

  ](http://udel.edu/~ghuang/iros19-vins-workshop/index.html) workshop at [IROS 2019](https://www.iros2019.org/). The

  submitted workshop paper can be found at [this](http://udel.edu/~ghuang/iros19-vins-workshop/papers/06.pdf) link.

* **August 21, 2019** - Open sourced [ov_maplab](https://github.com/rpng/ov_maplab) for interfacing OpenVINS with

  the [maplab](https://github.com/ethz-asl/maplab) library.

* **August 15, 2019** - Initial release of OpenVINS repository and documentation website!



## Project Features



* Sliding window visual-inertial MSCKF

* Modular covariance type system

* Comprehensive documentation and derivations

* Extendable visual-inertial simulator

    * On manifold SE(3) b-spline

    * Arbitrary number of cameras

    * Arbitrary sensor rate

    * Automatic feature generation

* Five different feature representations

    1. Global XYZ

    2. Global inverse depth

    3. Anchored XYZ

    4. Anchored inverse depth

    5. Anchored MSCKF inverse depth

    6. Anchored single inverse depth

* Calibration of sensor intrinsics and extrinsics

    * Camera to IMU transform

    * Camera to IMU time offset

    * Camera intrinsics

    * Inertial intrinsics (including g-sensitivity)

* Environmental SLAM feature

    * OpenCV ARUCO tag SLAM features

    * Sparse feature SLAM features

* Visual tracking support

    * Monocular camera

    * Stereo camera (synchronized)

    * Binocular cameras (synchronized)

    * KLT or descriptor based

    * Masked tracking

* Static and dynamic state initialization

* Zero velocity detection and updates

* Out of the box evaluation on EuRocMav, TUM-VI, UZH-FPV, KAIST Urban and other VIO datasets

* Extensive evaluation suite (ATE, RPE, NEES, RMSE, etc..)



## Codebase Extensions



* **[ov_plane](https://github.com/rpng/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](https://github.com/rpng/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](https://github.com/rpng/ov_maplab)** - This codebase contains the interface wrapper for exporting

  visual-inertial runs from [OpenVINS](https://github.com/rpng/open_vins) into the ViMap structure taken

  by [maplab](https://github.com/ethz-asl/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](https://github.com/rpng/ov_secondary)** - This is an example secondary thread which provides loop

  closure in a loosely coupled manner for [OpenVINS](https://github.com/rpng/open_vins). This is a modification of the

  code originally developed by the HKUST aerial robotics group and can be found in

  their [VINS-Fusion](https://github.com/HKUST-Aerial-Robotics/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.



## Demo Videos





   





   





   





   





   









   





   





   





   




## Credit / Licensing



This code was written by the [Robot Perception and Navigation Group (RPNG)](https://sites.udel.edu/robot/) 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:



```txt

@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)](https://www.gnu.org/licenses/gpl-3.0.txt).

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](https://choosealicense.com/licenses/gpl-3.0/); this is not legal advice).