Friday October 09, 2009. 4:00 pm
Room 302 HRBB

On the Consistency of EKF-based Simultaneous Localization and Mapping of mobile robots

Ali Agha.
Department of Computer Science and Engineering, Texas A&M University

Abstract

In this talk a new strategy for handling the observation information of a bearing-range sensor throughout the filtering process of EKF-SLAM is introduced. This new strategy is advised based on a thorough consistency analysis and aims to improve the process consistency while reducing the computational cost. At first, three different possible observation models are introduced for the EKF-SLAM solution for a robot equipped with a bearing-range sensor. General form of the covariance matrix and the level of inconsistency in the robot orientation estimate is then calculated for these variants, and comparing the estimation results, it is proposed to use the bearing and range information of a feature in the initialization step of EKF-SLAM. However, it is recommended to use only the bearing information to perform other iteration steps. The simulation observations verify that the new strategy yields to more consistent estimates, both for the robot and the features. Moreover, through the proposed consistency analysis it is shown that since the source of consistency improvement is independent from the choice of the motion model, it gives us an advantage over other existing methods that assume a specific motion model for consistency improvement.

Biography

Ali Agha received his Bachlor’s (2005) and Master’s (2008) degrees in Control Engineering, both as the first-best student, from Tabriz University and K.N. Toosi University of Technology, respectively. He is selected as the distinguished graduate student of K.N. Toosi University of Technology in 2008. Also, he won several national and international prizes with Resquake team in rescue robots competitions. He is currently a PhD student at the Department of Computer Science and Engineering (CSE) at Texas A&M University. His research interests include optimization, estimation and filtering for robotics systems and computer vision.

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