Localization and Map Building Based on Particle Filter and Unscented Kalman Filter for an AUV

被引：0

作者：

He, Bo ^{[1
]}

Yang, Lili ^{[1
]}

Yang, Ke ^{[1
]}

Wang, Yitong ^{[1
]}

Yu, Nini ^{[1
]}

Lue, Chunrong ^{[1
]}

机构：

[1] Ocean Univ China, Sch Informat Sci & Engn, Qingdao 266100, Peoples R China

来源：

ICIEA: 2009 4TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, VOLS 1-6 | 2009年

关键词：

localization and map building; AUV; particle filter;

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：

Simultaneous localization and mapping (SLAM) is of prime importance for navigation problem of autonomous underwater vehicle. Currently EKF-based SLAM and particle filter-based SLAM are prevalent methods though they have their own deficiency respectively. In this paper a modified RBPF method is proposed to apply in navigation and localization for our underwater vehicle, C-RANGER. Unscented Kalman filter instead of extended Kalman filter is used to incorporate the current observations as well as the historical observations into the proposal distribution. The simulation results show that the improved algorithm is more accurate and reliable while it is used to estimate the pose of AUV and locations of features.

引用

页码：3917 / 3921

页数：5

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