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
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Lenguaje:EN
Publicado: IEEE 2020
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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
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AT weiminzhang lcpfaparticlefilterlidarslamsystemwithloopdetectionandcorrection
AT zhuoyao lcpfaparticlefilterlidarslamsystemwithloopdetectionandcorrection
AT yongliangshi lcpfaparticlefilterlidarslamsystemwithloopdetectionandcorrection
AT fangxingli lcpfaparticlefilterlidarslamsystemwithloopdetectionandcorrection
AT qianghuang lcpfaparticlefilterlidarslamsystemwithloopdetectionandcorrection
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