LCPF: A Particle Filter Lidar SLAM System With Loop Detection and Correction

A globally consistent map is the basis of indoor robot localization and navigation. However, map built by Rao-Blackwellized Particle Filter (RBPF) doesn’t have high global consistency which is not suitable for long-term application in large scene. To address the problem, we present an imp...

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Autores principales:	Fuyu Nie, Weimin Zhang, Zhuo Yao, Yongliang Shi, Fangxing Li, Qiang Huang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Simultaneous localization and mapping mobile robots indoor navigation particle filter loop detection dynamic submap segementation Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/9816db6acefa4116934dcd2c537163f7
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spelling	oai:doaj.org-article:9816db6acefa4116934dcd2c537163f72021-11-19T00:02:55ZLCPF: A Particle Filter Lidar SLAM System With Loop Detection and Correction2169-353610.1109/ACCESS.2020.2968353https://doaj.org/article/9816db6acefa4116934dcd2c537163f72020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/8964341/https://doaj.org/toc/2169-3536A globally consistent map is the basis of indoor robot localization and navigation. However, map built by Rao-Blackwellized Particle Filter (RBPF) doesn’t have high global consistency which is not suitable for long-term application in large scene. To address the problem, we present an improved RBPF Lidar SLAM system with loop detection and correction named LCPF. The efficiency and accuracy of loop detection depend on the segmentation of submaps. Instead of dividing the submap at fixed number of laser scan like existing method, Dynamic Submap Segmentation is proposed in LCPF. This segmentation algorithm decreases the error inside the submap by splitting the submap where there is high scan match error and later rectifies the error by an improved pose graph optimization between submaps. In order to segment the submap at appropriate point, when to create a new submap is determined by both the accumulation of scan match error and the particle distribution. Furthermore, LCPF uses branch and bound algorithm as basic detector for loop detection and multiple criteria to judge the reliability of a loop. In the criteria, a novel parameter called usable ratio was proposed to measure the useful information that a laser scan containing. Finally, comparisons to existing 2D-Lidar mapping algorithm are performed with a series of open dataset simulations and real robot experiments to demonstrate the effectiveness of LCPF.Fuyu NieWeimin ZhangZhuo YaoYongliang ShiFangxing LiQiang HuangIEEEarticleSimultaneous localization and mappingmobile robotsindoor navigationparticle filterloop detectiondynamic submap segementationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 20401-20412 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Simultaneous localization and mapping mobile robots indoor navigation particle filter loop detection dynamic submap segementation Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Simultaneous localization and mapping mobile robots indoor navigation particle filter loop detection dynamic submap segementation Electrical engineering. Electronics. Nuclear engineering TK1-9971 Fuyu Nie Weimin Zhang Zhuo Yao Yongliang Shi Fangxing Li Qiang Huang LCPF: A Particle Filter Lidar SLAM System With Loop Detection and Correction
description	A globally consistent map is the basis of indoor robot localization and navigation. However, map built by Rao-Blackwellized Particle Filter (RBPF) doesn’t have high global consistency which is not suitable for long-term application in large scene. To address the problem, we present an improved RBPF Lidar SLAM system with loop detection and correction named LCPF. The efficiency and accuracy of loop detection depend on the segmentation of submaps. Instead of dividing the submap at fixed number of laser scan like existing method, Dynamic Submap Segmentation is proposed in LCPF. This segmentation algorithm decreases the error inside the submap by splitting the submap where there is high scan match error and later rectifies the error by an improved pose graph optimization between submaps. In order to segment the submap at appropriate point, when to create a new submap is determined by both the accumulation of scan match error and the particle distribution. Furthermore, LCPF uses branch and bound algorithm as basic detector for loop detection and multiple criteria to judge the reliability of a loop. In the criteria, a novel parameter called usable ratio was proposed to measure the useful information that a laser scan containing. Finally, comparisons to existing 2D-Lidar mapping algorithm are performed with a series of open dataset simulations and real robot experiments to demonstrate the effectiveness of LCPF.
format	article
author	Fuyu Nie Weimin Zhang Zhuo Yao Yongliang Shi Fangxing Li Qiang Huang
author_facet	Fuyu Nie Weimin Zhang Zhuo Yao Yongliang Shi Fangxing Li Qiang Huang
author_sort	Fuyu Nie
title	LCPF: A Particle Filter Lidar SLAM System With Loop Detection and Correction
title_short	LCPF: A Particle Filter Lidar SLAM System With Loop Detection and Correction
title_full	LCPF: A Particle Filter Lidar SLAM System With Loop Detection and Correction
title_fullStr	LCPF: A Particle Filter Lidar SLAM System With Loop Detection and Correction
title_full_unstemmed	LCPF: A Particle Filter Lidar SLAM System With Loop Detection and Correction
title_sort	lcpf: a particle filter lidar slam system with loop detection and correction
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/9816db6acefa4116934dcd2c537163f7
work_keys_str_mv	AT fuyunie lcpfaparticlefilterlidarslamsystemwithloopdetectionandcorrection AT weiminzhang lcpfaparticlefilterlidarslamsystemwithloopdetectionandcorrection AT zhuoyao lcpfaparticlefilterlidarslamsystemwithloopdetectionandcorrection AT yongliangshi lcpfaparticlefilterlidarslamsystemwithloopdetectionandcorrection AT fangxingli lcpfaparticlefilterlidarslamsystemwithloopdetectionandcorrection AT qianghuang lcpfaparticlefilterlidarslamsystemwithloopdetectionandcorrection
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LCPF: A Particle Filter Lidar SLAM System With Loop Detection and Correction

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