Title: Simultaneous localisation and mapping of a mobile robot via interlaced extended Kalman filter

Authors: Stefano Panzieri, Federica Pascucci, Roberto Setola

Addresses: Dipartimento di Informatica e Automazione, Universita degli Studi 'Roma TRE', Via della Vasca Navale, 79, Roma 00146, Italy. ' Dipartimento di Informatica e Automazione, Universita degli Studi 'Roma TRE', Via della Vasca Navale, 79, Roma 00146, Italy. ' Complex Systems and Security Laboratory, Universita Campus Bio-Medico, Via E. Longoni 83, Roma 00155, Italy

Abstract: A crucial task for automatic explorations is the ability for a robot to real-time estimate its position in an unknown environment. To this end, the robot is required to simultaneously localise itself and to build a map of the surroundings (Simultaneous Localisation and Mapping (SLAM) problem). This problem represents an interesting test-bed for non-linear estimator techniques. In this paper we propose to illustrate a solution based on the Extended Kalman Filter (EKF) approach, able to considerably reduce the computational burden and memory occupancy requirements, both of them representing two of the main drawbacks for this class of solutions. Specifically, we adopt the Interlaced Extended Kalman Filter (IEKF) formulation where the whole estimation problem is decomposed into a number of semi-autonomous subproblems. To partially compensate the decoupling errors introduced, process and measurements covariance matrices are suitably augmented. Two different implementations are analysed and compared with traditional EKF-based approaches. Experimental results emphasise that, even if the IEKF formulations suffer for a slight degraded estimation, they dramatically reduce computational burden. In this way, IEKF solutions to SLAM problems appear to be a good trade-off between accuracy and computational requirements, making it suitable for real time implementations.

Keywords: nonlinear estimation; Kalman filter; EKF; extended Kalman filter; interlaced Kalman filter; robot localisation; SLAM; robot mapping; mobile robots; nonlinear observers.

DOI: 10.1504/IJMIC.2008.021001

International Journal of Modelling, Identification and Control, 2008 Vol.4 No.1, pp.68 - 78

Published online: 30 Oct 2008 *

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