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SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time

The calibration problem of binocular stereo vision rig is critical for its practical application. However, most existing calibration methods are based on manual off-line algorithms for specific reference targets or patterns. In this paper, we propose a novel simultaneous localization and mapping (SL...

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Autores principales: Yin, Hesheng, Ma, Zhe, Zhong, Ming, Wu, Kuan, Wei, Yuteng, Guo, Junlong, Huang, Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038334/
https://www.ncbi.nlm.nih.gov/pubmed/31979170
http://dx.doi.org/10.3390/s20030621
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author Yin, Hesheng
Ma, Zhe
Zhong, Ming
Wu, Kuan
Wei, Yuteng
Guo, Junlong
Huang, Bo
author_facet Yin, Hesheng
Ma, Zhe
Zhong, Ming
Wu, Kuan
Wei, Yuteng
Guo, Junlong
Huang, Bo
author_sort Yin, Hesheng
collection PubMed
description The calibration problem of binocular stereo vision rig is critical for its practical application. However, most existing calibration methods are based on manual off-line algorithms for specific reference targets or patterns. In this paper, we propose a novel simultaneous localization and mapping (SLAM)-based self-calibration method designed to achieve real-time, automatic and accurate calibration of the binocular stereo vision (BSV) rig’s extrinsic parameters in a short period without auxiliary equipment and special calibration markers, assuming the intrinsic parameters of the left and right cameras are known in advance. The main contribution of this paper is to use the SLAM algorithm as our main tool for the calibration method. The method mainly consists of two parts: SLAM-based construction of 3D scene point map and extrinsic parameter calibration. In the first part, the SLAM mainly constructs a 3D feature point map of the natural environment, which is used as a calibration area map. To improve the efficiency of calibration, a lightweight, real-time visual SLAM is built. In the second part, extrinsic parameters are calibrated through the 3D scene point map created by the SLAM. Ultimately, field experiments are performed to evaluate the feasibility, repeatability, and efficiency of our self-calibration method. The experimental data shows that the average absolute error of the Euler angles and translation vectors obtained by our method relative to the reference values obtained by Zhang’s calibration method does not exceed 0.5˚ and 2 mm, respectively. The distribution range of the most widely spread parameter in Euler angles is less than 0.2˚ while that in translation vectors does not exceed 2.15 mm. Under the general texture scene and the normal driving speed of the mobile robot, the calibration time can be generally maintained within 10 s. The above results prove that our proposed method is reliable and has practical value.
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spelling pubmed-70383342020-03-09 SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time Yin, Hesheng Ma, Zhe Zhong, Ming Wu, Kuan Wei, Yuteng Guo, Junlong Huang, Bo Sensors (Basel) Article The calibration problem of binocular stereo vision rig is critical for its practical application. However, most existing calibration methods are based on manual off-line algorithms for specific reference targets or patterns. In this paper, we propose a novel simultaneous localization and mapping (SLAM)-based self-calibration method designed to achieve real-time, automatic and accurate calibration of the binocular stereo vision (BSV) rig’s extrinsic parameters in a short period without auxiliary equipment and special calibration markers, assuming the intrinsic parameters of the left and right cameras are known in advance. The main contribution of this paper is to use the SLAM algorithm as our main tool for the calibration method. The method mainly consists of two parts: SLAM-based construction of 3D scene point map and extrinsic parameter calibration. In the first part, the SLAM mainly constructs a 3D feature point map of the natural environment, which is used as a calibration area map. To improve the efficiency of calibration, a lightweight, real-time visual SLAM is built. In the second part, extrinsic parameters are calibrated through the 3D scene point map created by the SLAM. Ultimately, field experiments are performed to evaluate the feasibility, repeatability, and efficiency of our self-calibration method. The experimental data shows that the average absolute error of the Euler angles and translation vectors obtained by our method relative to the reference values obtained by Zhang’s calibration method does not exceed 0.5˚ and 2 mm, respectively. The distribution range of the most widely spread parameter in Euler angles is less than 0.2˚ while that in translation vectors does not exceed 2.15 mm. Under the general texture scene and the normal driving speed of the mobile robot, the calibration time can be generally maintained within 10 s. The above results prove that our proposed method is reliable and has practical value. MDPI 2020-01-22 /pmc/articles/PMC7038334/ /pubmed/31979170 http://dx.doi.org/10.3390/s20030621 Text en © 2020 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
Yin, Hesheng
Ma, Zhe
Zhong, Ming
Wu, Kuan
Wei, Yuteng
Guo, Junlong
Huang, Bo
SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time
title SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time
title_full SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time
title_fullStr SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time
title_full_unstemmed SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time
title_short SLAM-Based Self-Calibration of a Binocular Stereo Vision Rig in Real-Time
title_sort slam-based self-calibration of a binocular stereo vision rig in real-time
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038334/
https://www.ncbi.nlm.nih.gov/pubmed/31979170
http://dx.doi.org/10.3390/s20030621
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