@inproceedings{cb374dd4921549a48e880eda1b5500b8,
title = "Tightly Coupled VINS Based on Adaptive Nonlinear Complementary Filtering",
abstract = "The basic idea of the tightly coupled VINS (Visual Inertial Navigation System) is to make up for the lack of scale information of monocular vision odometer by IMU, use the monocular vision odometer to correct the bias of IMU and suppose the noise of IMU is Gaussian white noise. In view of the inability of the tightly coupled VINS to correct the noise caused by the environmental impact on the IMU, this paper presents an improved IMU data processing algorithm based on the adaptive nonlinear complementary filter and tests the robust performance of the improved IMU data processing algorithm in a framework of the tightly coupled VINS based on optimization proposed in VINS-Mono. The robust performance of the improved IMU data processing algorithm can correct the noise caused by the environmental impact on the IMU and improve the positioning accuracy, when the visual information is limited and the effect of IMU noise on positioning accuracy is enhanced.",
keywords = "Adaptive, Complementary filter, Multi-sensor fusion, Position, SLAM",
author = "Huimin Wang and Zhen Zhang and Hai Zhang",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 17th Chinese Intelligent Systems Conference, CISC 2021 ; Conference date: 16-10-2021 Through 17-10-2021",
year = "2022",
doi = "10.1007/978-981-16-6328-4\_35",
language = "英语",
isbn = "9789811663277",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "328--337",
editor = "Yingmin Jia and Weicun Zhang and Yongling Fu and Zhiyuan Yu and Song Zheng",
booktitle = "Proceedings of 2021 Chinese Intelligent Systems Conference",
address = "德国",
}