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A Floating-point Extended Kalman Filter Implementation for Autonomous Mobile Robots
2008
Journal of Signal Processing Systems
Localization and Mapping are two of the most important capabilities for autonomous mobile robots and have been receiving considerable attention from the scientific computing community over the last 10 years. One of the most efficient methods to address these problems is based on the use of the Extended Kalman Filter (EKF). The EKF simultaneously estimates a model of the environment (map) and the position of the robot based on odometric and exteroceptive sensor information. As this algorithm
doi:10.1007/s11265-008-0257-8
fatcat:d7lbdmtm2vgs5bkuebsdisfgxi