Triangulation-Based Indoor Robot Localization Using Extended FIR/Kalman Filtering

被引：0

作者：

Granados-Cruz, Moises ^{[1
]}

Pomarico-Franquiz, Juan ^{[1
]}

Shmaliy, Yuriy S. ^{[1
]}

Morales-Mendoza, Luis J. ^{[2
]}

机构：

[1] Univ Guanajuato, Dept Elect Engn, Salamanca 36885, Mexico

[2] Univ Veracruzana, Dept Elect, Poza Rica 93390, Mexico

来源：

2014 11TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, COMPUTING SCIENCE AND AUTOMATIC CONTROL (CCE) | 2014年

关键词：

PARTICLE FILTERS;

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：

A combined extended finite impulse response (EFIR) and Kalman (EFIR/Kalman) algorithm is proposed for mobile robot localization via triangulation. A distinctive advantage of the EFIR algorithm is that it completely ignores the noise statistics which are typically not well known to the engineer. Instead, it requires an optimal averaging interval of N-opt points. To run this algorithm, several initial Kalman estimates are used for the roughly set noise covariances. We consider a mobile robot travelling on an indoor floorspace and localized via triangulation with three nodes in a view. We show that the EFIR/Kalman filter is more accurate than the extended Kalman filter under the uncertain noise statistics and initial state.

引用

页数：5

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