A survey of back-end optimization method for graph-based SLAM under large-scale environment

Zhongli Wang; Jie Zhao; Hegao Cai

doi:10.11918/j.issn.0367-6234.2015.07.002

A survey of back-end optimization method for graph-based SLAM under large-scale environment

Zhongli Wang^*
, Jie Zhao
, Hegao Cai

^*Corresponding author for this work

School of Mechatronics Engineering

Beijing Jiaotong University
Harbin Institute of Technology

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

Graph optimization-based SLAM is the main method under large-scale environment. The framework of this method is composed of two parts, front-end and back-end. Be a continuation paper of our previous one, the four main back-end optimization approaches, which include least square, stochastic gradient descent, relaxation, manifold optimization, and the correspondent literatures are introduced, and two map evaluation methods are presented, that is χ² error based and MSE error based. The trends of graph optimization-based SLAM method are predicted.

Original language	English
Pages (from-to)	20-25
Number of pages	6
Journal	Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology
Volume	47
Issue number	7
DOIs	https://doi.org/10.11918/j.issn.0367-6234.2015.07.002
State	Published - 30 Jul 2015

Keywords

Graph modeling
Graph optimization
Large-scale environment
Mobile robot
Simultaneous localization and mapping (SLAM)

Access to Document

10.11918/j.issn.0367-6234.2015.07.002

Cite this

@article{3ff829dabf2e4c2bad95971688d97364,

title = "A survey of back-end optimization method for graph-based SLAM under large-scale environment",

abstract = "Graph optimization-based SLAM is the main method under large-scale environment. The framework of this method is composed of two parts, front-end and back-end. Be a continuation paper of our previous one, the four main back-end optimization approaches, which include least square, stochastic gradient descent, relaxation, manifold optimization, and the correspondent literatures are introduced, and two map evaluation methods are presented, that is χ2 error based and MSE error based. The trends of graph optimization-based SLAM method are predicted.",

keywords = "Graph modeling, Graph optimization, Large-scale environment, Mobile robot, Simultaneous localization and mapping (SLAM)",

author = "Zhongli Wang and Jie Zhao and Hegao Cai",

year = "2015",

month = jul,

day = "30",

doi = "10.11918/j.issn.0367-6234.2015.07.002",

language = "英语",

volume = "47",

pages = "20--25",

journal = "Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology",

issn = "0367-6234",

publisher = "Harbin Institute of Technology",

number = "7",

}

TY - JOUR

T1 - A survey of back-end optimization method for graph-based SLAM under large-scale environment

AU - Wang, Zhongli

AU - Zhao, Jie

AU - Cai, Hegao

PY - 2015/7/30

Y1 - 2015/7/30

N2 - Graph optimization-based SLAM is the main method under large-scale environment. The framework of this method is composed of two parts, front-end and back-end. Be a continuation paper of our previous one, the four main back-end optimization approaches, which include least square, stochastic gradient descent, relaxation, manifold optimization, and the correspondent literatures are introduced, and two map evaluation methods are presented, that is χ2 error based and MSE error based. The trends of graph optimization-based SLAM method are predicted.

AB - Graph optimization-based SLAM is the main method under large-scale environment. The framework of this method is composed of two parts, front-end and back-end. Be a continuation paper of our previous one, the four main back-end optimization approaches, which include least square, stochastic gradient descent, relaxation, manifold optimization, and the correspondent literatures are introduced, and two map evaluation methods are presented, that is χ2 error based and MSE error based. The trends of graph optimization-based SLAM method are predicted.

KW - Graph modeling

KW - Graph optimization

KW - Large-scale environment

KW - Mobile robot

KW - Simultaneous localization and mapping (SLAM)

UR - https://www.scopus.com/pages/publications/84943563903

U2 - 10.11918/j.issn.0367-6234.2015.07.002

DO - 10.11918/j.issn.0367-6234.2015.07.002

M3 - 文章

AN - SCOPUS:84943563903

SN - 0367-6234

VL - 47

SP - 20

EP - 25

JO - Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology

JF - Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology

IS - 7

ER -

A survey of back-end optimization method for graph-based SLAM under large-scale environment

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this