IRobot Self-localization Using EKF

被引：0

作者：

Zhao, Shuqiang ^{[1
]}

Gu, Jason ^{[2
]}

Ou, Yongsheng ^{[3
]}

Zhang, Wei ^{[1
]}

Pu, Jiexin ^{[4
]}

Peng, Heather ^{[5
]}

机构：

[1] Shandong Univ, Jinan, Shandong, Peoples R China

[2] Dalhousie Univ, Halifax, NS, Canada

[3] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Guangdong, Peoples R China

[4] Henan Univ Sci & Technol, Luoyang, Henan Province, Peoples R China

[5] Mem Univ Newfoundland, St John, NF, Canada

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA) | 2016年

基金：

中国国家自然科学基金;

关键词：

EKF; self-localization; motion model; odometry; gyro;

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：

Self-Localization plays an important role in the mobile robot autonomous navigation. The Wheel Mobile robot usually contains a large number of different sensors, such as odometry, gyro, laser, camera and so on. All these sensors provide the information of robot localization and all these information should be considered for the optimal location. However, for the cost of the iRobot, we could not be equipped with a lot of sensors. We have only encoder sensor and gyro sensor. So this paper researches mobile robot localization only using odometer and gyro sensor based on Extended Kalman Filter (EKF). The method is that the iRobot fuses the messages from encoder sensor and gyro sensor by EKF theory, which can collect the errors that obtained the robot's orientation and position. The experiment results appear that the proposed self-localization method is effective and feasible.

引用

页码：801 / 806

页数：6

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