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FPL
2007
Springer

A floating-point Extended Kalman Filter implementation for autonomous mobile robots

14 years 5 months ago
A floating-point Extended Kalman Filter implementation for autonomous mobile robots
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 demands a considerable amount of computation, it is usually executed on high end PCs coupled to the robot. In this work we present an FPGA-based architecture for the EKF
Vanderlei Bonato, Eduardo Marques, George A. Const
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where FPL
Authors Vanderlei Bonato, Eduardo Marques, George A. Constantinides
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