Robust Robot Localization by Sensors with Different Degree of Accuracy

被引：6

作者：

Ippoliti, Gianluca ^{[1
]}

La Manna, Alessia ^{[1
]}

Longhi, Sauro ^{[1
]}

机构：

[1] Univ Politecn Marche, Dipartimento Ingn Informat Gest & Automaz, I-60131 Ancona, Italy

来源：

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS | 2009年 / 56卷 / 03期

关键词：

Mobile robot localization; Kalman filtering; Sensor fusion; Autonomous systems; Sensors; Mechatronics; MOBILE; PRECISION; AUTONOMY; SLAM;

D O I：

10.1007/s10846-009-9311-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper the robust robot localization problem with respect to uncertainties on environment features is formulated in a stochastic setting, and an Extended Kalman Filtering (EKF) approach is proposed for the integration of odometric, gyroscopic, and sonar measures. As gyroscopic readings are much more reliable than the other ones, the localization algorithm gives rise to a nearly singular EKF. This problem is dealt with by defining a reduced order nonsingular EKF. The robust solution has been implemented and tested on a powered wheelchair.

引用

页码：259 / 276

页数：18

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