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The Kalman filter is widely used as an estimator in many modern applications. In the case where its implementation in hardware is required, the computational complexity of the algorithm dictates the use of many resources. This paper presents an approximation of the conventional Kalman filter by using Taylor expansion and matrix calculus in order to remove the hardware expensive part of the algorithm...
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...
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