Extended Kalman filter-based mobile robot localization with intermittent measurements

Hamzah Ahmad, Toru Namerikawa

研究成果: Article査読

52 被引用数 (Scopus)

抄録

In this paper, a theoretical study on extended Kalman filter (EKF)-based mobile robot localization with intermittent measurements is examined by analysing the measurement innovation characteristics. Even if measurement data are unavailable and existence of uncertainties during mobile robot observations, it is suggested that the mobile robot can effectively estimate its location in an environment. This paper presents the uncertainties bounds of estimation by analysing the measurement innovation to preserve good estimations although some measurements data are sometimes missing. Theoretical analysis of the EKF is proposed to demonstrate the conditions when the problem occurred. From the analysis of measurement innovation, Jacobian transformation has been found as one of the main factors that affects the estimation performance. Besides that, the initial state covariance, process and measurement noises must be kept smaller to achieve better estimation results. The simulation and experimental results obtained are showing consistent behaviour as proposed in this paper.

本文言語English
ページ(範囲)113-126
ページ数14
ジャーナルSystems Science and Control Engineering
1
1
DOI
出版ステータスPublished - 2013

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization
  • Artificial Intelligence

フィンガープリント 「Extended Kalman filter-based mobile robot localization with intermittent measurements」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル