FPGA implementation of a sequential Extended Kalman Filter algorithm applied to mobile robotics localization problem

Cruz, Sergio Salazar; Muñoz, Daniel M.; Conde, M.; Llanos, Carlos H.; Borges, Geovany A.

doi:10.1109/lascas.2013.6519021

Cited by 15 publications

(1 citation statement)

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“…and disturbed measurement is linear, namely, the Kalman filter is a linear filter that provides a prediction and an update step [52]. The Extended Kalman Filter [EKF] [53] is an extension of Kalman Filter, which provides the non-linear solutions using a first order Taylor expansion [54]. However, this reduces the accuracy of estimation result.…”

Section: The Iterated Sigma Point Kalman Filter Refinementmentioning

confidence: 99%

Robust Features and Accurate Inliers Detection Framework: Application to Stereo Ego-motion Estimation

2016

KSII TIIS

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In this paper, an innovative robust feature detection and matching strategy for visual odometry based on stereo image sequence is proposed. First, a sparse multiscale 2D local invariant feature detection and description algorithm AKAZE is adopted to extract the interest points. A robust feature matching strategy is introduced to match AKAZE descriptors. In order to remove the outliers which are mismatched features or on dynamic objects, an improved random sample consensus outlier rejection scheme is presented. Thus the proposed method can be applied to dynamic environment. Then, geometric constraints are incorporated into the motion estimation without time-consuming 3-dimensional scene reconstruction. Last, an iterated sigma point Kalman Filter is adopted to refine the motion results. The presented ego-motion scheme is applied to benchmark datasets and compared with state-of-the-art approaches with data captured on campus in a considerably cluttered environment, where the superiorities are proved.

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Section: The Iterated Sigma Point Kalman Filter Refinementmentioning

confidence: 99%