OpenVINS: A Research Platform for Visual-Inertial Estimation

By Patrick Geneva, Kevin Eckenhoff, Woosik Lee, Yulin Yang, Guoquan Huang. IEEE International Conference on Robotics and Automation, 2020

740 citations recorded by the source corpus.

In this paper, we present an open platform, termed OpenVINS, for visual-inertial estimation research for both the academic community and practitioners from industry. The open sourced codebase provides a foundation for researchers and engineers to quickly start developing new capabilities for their visual-inertial systems. This codebase has out of the box support for commonly desired visual-inertial estimation features, which include: (i) on-manifold sliding window Kalman filter, (ii) online camera intrinsic and extrinsic calibration, (iii) camera to inertial sensor time offset calibration, (iv) SLAM landmarks with different representations and consistent First-Estimates Jacobian (FEJ) treatments, (v) modular type system for state management, (vi) extendable visual-inertial system simulator, and (vii) extensive toolbox for algorithm evaluation. Moreover, we have also focused on detailed documentation and theoretical derivations to support rapid development and research, which are greatly lacked in the current open sourced algorithms. Finally, we perform comprehensive validation of the proposed OpenVINS against state-of-the-art open sourced algorithms, showing its competing estimation performance.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology

View Paper: https://www.semanticscholar.org/paper/cb6360f21255834297e32826bff6366a769b49e9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Daniela Rus
2. Carlo Ratti
3. Brendan Englot
4. Tixiao Shan
5. Fu Zhang
6. Jiarong Lin
7. Chen Wang
8. Lihua Xie

In Computer Vision and Pattern Recognition

1. Daniela Rus
2. Carlo Ratti
3. Brendan Englot
4. Tixiao Shan
5. Fu Zhang
6. Jiarong Lin
7. Chen Wang
8. Lihua Xie

In Geology

1. Carlo Ratti
2. Daniela Rus
3. Brendan Englot
4. Tixiao Shan

Citations in the corpus

• LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping (2021, methodology): “Among the most popular publiclyavailable VIO pipelines, MSCKF [3], ROVIO [4] and OpenVINS [5] are filter-based, and OKVIS [6] and VINS-Mono [7] are optimization-based.”