The SLAM problem: a survey

被引：157

作者：

Aulinas, Josep ^{[1
]}

Petillot, Yvan ^{[2
]}

Salvi, Joaquim ^{[1
]}

Llado, Xavier ^{[1
]}

机构：

[1] Univ Girona, Inst Informat & Applicat, Girona, Spain

[2] Heriot Watt Univ, Ocean Syst Lab, Edinburgh, Scotland

来源：

ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT | 2008年 / 184卷

关键词：

SLAM; Kalman filter; Particle Filter; Expectation Maximization;

D O I：

10.3233/978-1-58603-925-7-363

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper surveys the most recent published techniques in the field of Simultaneous Localization kind Mapping (SLAM). In particular it is focused on the existing techniques available to speed up the process, with the purpose to handel large scale scenarios. The main research field we plan to investigate is the filtering algorithms as a way of reducing the amount of data. It seems that almost all the current approaches can not perform consistent maps for large areas, mainly due to the increase of the computational cost and due to the uncertainties that become prohibitive, when the scenario becomes larger.

引用

页码：363 / +

页数：3

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