Using a Two-Stage Method to Reject False Loop Closures and Improve the Accuracy of Collaborative SLAM Systems

Loop-closure detection is an essential means to reduce accumulated errors of simultaneous localization and mapping (SLAM) systems. However, even false positive loop closures could seriously interfere and even corrupt the back-end optimization process. For a collaborative SLAM system that generally u...

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Autores principales:	Xiaoguo Zhang, Zihan Zhang, Qing Wang, Yuan Yang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	collaborative SLAM cooperative robots perceptual aliasing false positive loop closure pose graph fusion consistency check Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/9cbfe8c88b314369bf7a29622fbf7fad
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spelling	oai:doaj.org-article:9cbfe8c88b314369bf7a29622fbf7fad2021-11-11T15:38:58ZUsing a Two-Stage Method to Reject False Loop Closures and Improve the Accuracy of Collaborative SLAM Systems10.3390/electronics102126382079-9292https://doaj.org/article/9cbfe8c88b314369bf7a29622fbf7fad2021-10-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2638https://doaj.org/toc/2079-9292Loop-closure detection is an essential means to reduce accumulated errors of simultaneous localization and mapping (SLAM) systems. However, even false positive loop closures could seriously interfere and even corrupt the back-end optimization process. For a collaborative SLAM system that generally uses both intra-robot and inter-robot loop closures to optimize the pose graph, it is a tough job to reject those false positive loop closures without a reliable a priori knowledge of the relative pose transformation between robots. Aiming at this solving problem, this paper proposes a two-stage false positive loop-closure rejection method based on three types of consistency checks. Firstly, a multi-robot pose-graph optimization model is given which transforms the multi-robot pose optimization problem into a maximum likelihood estimation model. Then, the principle of the false positive loop-closure rejection method based on <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="italic">χ</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> test is proposed, in which clustering is used to reject those intra-robot false loop-closures in the first step, and a largest mutually consistent loop-based <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="italic">χ</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> test is constructed to reject inter-robot false loop closures in the second step. Finally, an open dataset and synthetic data are used to evaluate the performance of the algorithms. The experimental results demonstrate that our method improves the accuracy and robustness of the back-end pose-graph optimization with a strong ability to reject false positive loop closures, and it is not sensitive to the initial pose at the same time. In the Computer Science and Artificial Intelligence Lab (CSAIL) dataset, the absolute position error is reduced by 55.37% compared to the dynamic scaling covariance method, and the absolute rotation error is reduced by 77.27%; in the city10,000 synthetic dataset, the absolute position error is reduced by 89.37% compared to the pairwise consistency maximization (PCM) and the absolute rotation error is reduced by 97.9%.Xiaoguo ZhangZihan ZhangQing WangYuan YangMDPI AGarticlecollaborative SLAMcooperative robotsperceptual aliasingfalse positive loop closurepose graph fusionconsistency checkElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2638, p 2638 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	collaborative SLAM cooperative robots perceptual aliasing false positive loop closure pose graph fusion consistency check Electronics TK7800-8360
spellingShingle	collaborative SLAM cooperative robots perceptual aliasing false positive loop closure pose graph fusion consistency check Electronics TK7800-8360 Xiaoguo Zhang Zihan Zhang Qing Wang Yuan Yang Using a Two-Stage Method to Reject False Loop Closures and Improve the Accuracy of Collaborative SLAM Systems
description	Loop-closure detection is an essential means to reduce accumulated errors of simultaneous localization and mapping (SLAM) systems. However, even false positive loop closures could seriously interfere and even corrupt the back-end optimization process. For a collaborative SLAM system that generally uses both intra-robot and inter-robot loop closures to optimize the pose graph, it is a tough job to reject those false positive loop closures without a reliable a priori knowledge of the relative pose transformation between robots. Aiming at this solving problem, this paper proposes a two-stage false positive loop-closure rejection method based on three types of consistency checks. Firstly, a multi-robot pose-graph optimization model is given which transforms the multi-robot pose optimization problem into a maximum likelihood estimation model. Then, the principle of the false positive loop-closure rejection method based on <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="italic">χ</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> test is proposed, in which clustering is used to reject those intra-robot false loop-closures in the first step, and a largest mutually consistent loop-based <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="italic">χ</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> test is constructed to reject inter-robot false loop closures in the second step. Finally, an open dataset and synthetic data are used to evaluate the performance of the algorithms. The experimental results demonstrate that our method improves the accuracy and robustness of the back-end pose-graph optimization with a strong ability to reject false positive loop closures, and it is not sensitive to the initial pose at the same time. In the Computer Science and Artificial Intelligence Lab (CSAIL) dataset, the absolute position error is reduced by 55.37% compared to the dynamic scaling covariance method, and the absolute rotation error is reduced by 77.27%; in the city10,000 synthetic dataset, the absolute position error is reduced by 89.37% compared to the pairwise consistency maximization (PCM) and the absolute rotation error is reduced by 97.9%.
format	article
author	Xiaoguo Zhang Zihan Zhang Qing Wang Yuan Yang
author_facet	Xiaoguo Zhang Zihan Zhang Qing Wang Yuan Yang
author_sort	Xiaoguo Zhang
title	Using a Two-Stage Method to Reject False Loop Closures and Improve the Accuracy of Collaborative SLAM Systems
title_short	Using a Two-Stage Method to Reject False Loop Closures and Improve the Accuracy of Collaborative SLAM Systems
title_full	Using a Two-Stage Method to Reject False Loop Closures and Improve the Accuracy of Collaborative SLAM Systems
title_fullStr	Using a Two-Stage Method to Reject False Loop Closures and Improve the Accuracy of Collaborative SLAM Systems
title_full_unstemmed	Using a Two-Stage Method to Reject False Loop Closures and Improve the Accuracy of Collaborative SLAM Systems
title_sort	using a two-stage method to reject false loop closures and improve the accuracy of collaborative slam systems
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9cbfe8c88b314369bf7a29622fbf7fad
work_keys_str_mv	AT xiaoguozhang usingatwostagemethodtorejectfalseloopclosuresandimprovetheaccuracyofcollaborativeslamsystems AT zihanzhang usingatwostagemethodtorejectfalseloopclosuresandimprovetheaccuracyofcollaborativeslamsystems AT qingwang usingatwostagemethodtorejectfalseloopclosuresandimprovetheaccuracyofcollaborativeslamsystems AT yuanyang usingatwostagemethodtorejectfalseloopclosuresandimprovetheaccuracyofcollaborativeslamsystems
_version_	1718434686650286080

Using a Two-Stage Method to Reject False Loop Closures and Improve the Accuracy of Collaborative SLAM Systems

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