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Neves da Fonseca Cardoso Carreira, Fernando Paulo

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D019-D006-510B
0000-0002-9873-5050

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20152
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Subject: Mobile robots ×

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  • Complementary filter design with three frequency bands: robot attitude estimation
    Publication . Carreira, Fernando; Calado, João Manuel Ferreira; Cardeira, Carlos Batista; Oliveira, Paulo Jorge Coelho Ramalho
    This paper extents the by now classic sensor fusion complementary filter (CF) design, involving two sensors, to the case where three sensors that provide measurements in different bands are available. This paper shows that the use of classical CF techniques to tackle a generic three sensors fusion problem, based solely on their frequency domain characteristics, leads to a minimal realization, stable, sub-optimal solution, denoted as Complementary Filters3 (CF3). Then, a new approach for the estimation problem at hand is used, based on optimal linear Kalman filtering techniques. Moreover, the solution is shown to preserve the complementary property, i.e. the sum of the three transfer functions of the respective sensors add up to one, both in continuous and discrete time domains. This new class of filters are denoted as Complementary Kalman Filters3 (CKF3). The attitude estimation of a mobile robot is addressed, based on data from a rate gyroscope, a digital compass, and odometry. The experimental results obtained are reported.
    2015Conference object Restricted access Show more
  • Enhanced PCA-based localization using depth maps with missing data
    Publication . Carreira, Fernando; Calado, João Manuel Ferreira; Cardeira, Carlos; Oliveira, Paulo
    In this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.
    2015-02Journal article Restricted access Show more