@inproceedings{c1131ae338764bd2b814141bdca0c584,
title = "Closed-form integration of IMU error state covariance for optimization-based Visual-Inertial State Estimator",
abstract = "In this paper, we propose a closed-form IMU error state covariance integration method for optimization-based visual- inertial state estimator. We derive a closed-form solutions of the IMU error state covariance propagation in pre-integration process, yielding improved accuracy of IMU residual information matrix. Our visual-inertial state estimator is based on a tightly-coupled, sliding-window optimization framework, which jointly estimate the IMU states and landmarks and performing marginalization to limit the computational cost. Finally, the system are validated in park environment dataset, the result shows our proposed method is effective.",
keywords = "pre-integration, state estimator, visual-inertial",
author = "Xingbo Wang and Zhihong Peng and Lele Xi",
note = "Publisher Copyright: {\textcopyright} 2020 Technical Committee on Control Theory, Chinese Association of Automation.; 39th Chinese Control Conference, CCC 2020 ; Conference date: 27-07-2020 Through 29-07-2020",
year = "2020",
month = jul,
doi = "10.23919/CCC50068.2020.9189495",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "3783--3788",
editor = "Jun Fu and Jian Sun",
booktitle = "Proceedings of the 39th Chinese Control Conference, CCC 2020",
address = "United States",
}