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Online IMU Self-Calibration for Visual-Inertial Systems
Low-cost microelectro mechanical systems (MEMS)-based inertial measurement unit (IMU) measurements are usually affected by inaccurate scale factors, axis misalignments, and g-sensitivity errors. These errors may significantly influence the performance of visual-inertial methods. In this paper, we pr...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480050/ https://www.ncbi.nlm.nih.gov/pubmed/30987407 http://dx.doi.org/10.3390/s19071624 |
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author | Xiao, Yao Ruan, Xiaogang Chai, Jie Zhang, Xiaoping Zhu, Xiaoqing |
author_facet | Xiao, Yao Ruan, Xiaogang Chai, Jie Zhang, Xiaoping Zhu, Xiaoqing |
author_sort | Xiao, Yao |
collection | PubMed |
description | Low-cost microelectro mechanical systems (MEMS)-based inertial measurement unit (IMU) measurements are usually affected by inaccurate scale factors, axis misalignments, and g-sensitivity errors. These errors may significantly influence the performance of visual-inertial methods. In this paper, we propose an online IMU self-calibration method for visual-inertial systems equipped with a low-cost inertial sensor. The goal of our method is to concurrently perform 3D pose estimation and online IMU calibration based on optimization methods in unknown environments without any external equipment. To achieve this goal, we firstly develop a novel preintegration method that can handle the IMU intrinsic parameters error propagation. Then, we frame IMU calibration problem into general factors so that we can easily integrate the factors into the current graph-based visual-inertial frameworks and jointly optimize the IMU intrinsic parameters as well as the system states in a big bundle. We evaluate the proposed method with a publicly available dataset. Experimental results verify that the proposed approach is able to accurately calibrate all the considered parameters in real time, leading to significant improvement of estimation precision of visual-inertial system (VINS) compared with the estimation results with offline precalibrated IMU measurements. |
format | Online Article Text |
id | pubmed-6480050 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64800502019-04-29 Online IMU Self-Calibration for Visual-Inertial Systems Xiao, Yao Ruan, Xiaogang Chai, Jie Zhang, Xiaoping Zhu, Xiaoqing Sensors (Basel) Article Low-cost microelectro mechanical systems (MEMS)-based inertial measurement unit (IMU) measurements are usually affected by inaccurate scale factors, axis misalignments, and g-sensitivity errors. These errors may significantly influence the performance of visual-inertial methods. In this paper, we propose an online IMU self-calibration method for visual-inertial systems equipped with a low-cost inertial sensor. The goal of our method is to concurrently perform 3D pose estimation and online IMU calibration based on optimization methods in unknown environments without any external equipment. To achieve this goal, we firstly develop a novel preintegration method that can handle the IMU intrinsic parameters error propagation. Then, we frame IMU calibration problem into general factors so that we can easily integrate the factors into the current graph-based visual-inertial frameworks and jointly optimize the IMU intrinsic parameters as well as the system states in a big bundle. We evaluate the proposed method with a publicly available dataset. Experimental results verify that the proposed approach is able to accurately calibrate all the considered parameters in real time, leading to significant improvement of estimation precision of visual-inertial system (VINS) compared with the estimation results with offline precalibrated IMU measurements. MDPI 2019-04-04 /pmc/articles/PMC6480050/ /pubmed/30987407 http://dx.doi.org/10.3390/s19071624 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xiao, Yao Ruan, Xiaogang Chai, Jie Zhang, Xiaoping Zhu, Xiaoqing Online IMU Self-Calibration for Visual-Inertial Systems |
title | Online IMU Self-Calibration for Visual-Inertial Systems |
title_full | Online IMU Self-Calibration for Visual-Inertial Systems |
title_fullStr | Online IMU Self-Calibration for Visual-Inertial Systems |
title_full_unstemmed | Online IMU Self-Calibration for Visual-Inertial Systems |
title_short | Online IMU Self-Calibration for Visual-Inertial Systems |
title_sort | online imu self-calibration for visual-inertial systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480050/ https://www.ncbi.nlm.nih.gov/pubmed/30987407 http://dx.doi.org/10.3390/s19071624 |
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