Novel Information Matrix Sparsification Approach for Practical Implementation of Simultaneous Localization and Mapping

被引：2

作者：

Dong, Haiwei ^{[1
]}

Tang, Jun ^{[2
]}

Chen, Weidong ^{[3
]}

Nagano, Akinori ^{[1
]}

Luo, Zhiwei ^{[1
]}

机构：

[1] Kobe Univ, Grad Sch Engn, Dept Comp Sci & Syst Engn, Nada Ku, Kobe, Hyogo 6578501, Japan

[2] New Huadu Cooperat Ltd, Shanghai 200120, Peoples R China

[3] Shanghai Jiao Tong Univ, Dept Automat, Sch Elect Informat & Elect Engn, Shanghai 200240, Peoples R China

来源：

ADVANCED ROBOTICS | 2010年 / 24卷 / 5-6期

关键词：

Mobile robot; SLAM; information matrix; sparsification; consistency; FILTERS;

D O I：

10.1163/016918610X493624

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

Simultaneous localization and mapping (SLAM) is a fundamental issue in mobile robotics because it is the basis of higher-level tasks of robots. Recently, more and more research has been proposed that aims to enhance the efficiency of SLAM solutions from the viewpoint of the information matrix. This paper presents a novel, efficient SLAM approach by using the characters of the information matrix. Our approach eliminates many of the elements in the information matrix while maintaining the consistency. The large complex environment simulation, as well as outdoor car park experiment verifies the validity of our approach. The proposed sparsification method provides an efficient way to obtain a consistent estimation with provable upper bounds of sparsification errors. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2010

引用

页码：819 / 838

页数：20

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