Algorithms Analysis of Mobile Robot SLAM based on Kalman and Particle Filter

被引：0

作者：

Zhang, Feng ^{[1
]}

Li, Siqi ^{[2
]}

Yuan, Shuai ^{[1
,3
]}

Sun, Enze ^{[1
]}

Zhao, Languang ^{[1
]}

机构：

[1] Shenyang Jianzhu Univ, Informat & Control Engn Fac, Shenyang 110168, Liaoning, Peoples R China

[2] Shenyang Jianzhu Univ, Shenyang, Liaoning, Peoples R China

[3] Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang 110016, Liaoning, Peoples R China

来源：

2017 9TH INTERNATIONAL CONFERENCE ON MODELLING, IDENTIFICATION AND CONTROL (ICMIC 2017) | 2017年

关键词：

mobile robot; Simultaneous Localization and Mapping; EKF-SLAM; UKF-SLAM; FastSLAM; UFastSLAM;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Many algorithms of mobile robot SLAM (Simultaneous Localization and Mapping) have been researched at present, however, the SLAM algorithm of mobile robot based on probability is often used in the unknown environment. In this paper, two kinds of SLAM algorithms based on probability are analyzed and compared. One kind is the SLAM algorithm based on Kalman filter: extended Kalman filter SLAM (EKF-SLAM) and unscented Kalman filter SLAM algorithm (UKF-SLAM). Another kind is the SLAM algorithm based on Particle filter: FastSLAM and unscented FastSLAM (UFastSLAM) algorithm. The difference from the four algorithms of SLAM is illustrated in terms of principle and calculation accuracy. Finally, the simulation results show that the UFastSLAM algorithm is superior to other algorithms in robot path and landmark estimation.

引用

页码：1050 / 1055

页数：6

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