Browsing by Author "Araneda, Anita"

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    A STATISTICAL APPROACH TO SIMULTANEOUS MAPPING AND LOCALIZATION FOR MOBILE ROBOTS
    (INST MATHEMATICAL STATISTICS, 2007) Araneda, Anita; Fienberg, Stephen E.; Soto, Alvaro
    Mobile robots require basic information to navigate through an environment: they need to know where they are (localization) and they need to know where they are going. For the latter, robots need a map of the environment. Using sensors of a variety of forms, robots gather information as they move through in environment in order to build a map. In this paper we present a novel sampling algorithm to solving the simultaneous mapping and localization (SLAM) problem in indoor environments. We approach the problem from a Bayesian statistics perspective. The data correspond to a set of range tinder and odometer measurements, obtained at discrete time instants. We focus on the estimation of the posterior distribution over the space of possible maps given the data. By exploiting different factorizations of this distribution, we derive three sampling algorithms based oil importance sampling. We illustrate the results of our approach by testing the algorithms with two real data sets obtained through robot navigation inside office buildings at Carnegie Mellon University and the Pontificia Universidad Catolica de Chile.
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    An accelerated algorithm for density estimation in large databases using Gaussian mixtures
    (2007) Soto, Alvaro; Zavala, Felipe; Araneda, Anita
    Today, with the advances of computer storage and technology, there are huge datasets available, offering an opportunity to extract valuable information. Probabilistic approaches are specially suited to learn from data by representing knowledge as density functions. In this paper, we choose Gaussian mixture models (GMMs) to represent densities, as they possess great flexibility to adequate to a wide class of problems. The classical estimation approach for GMMs corresponds to the iterative algorithm of expectation maximization (EM). This approach, however, does not scale properly to meet the high demanding processing requirements of large databases. In this paper we introduce an EM-based algorithm, that solves the scalability problem. Our approach is based on the concept of data condensation which, in addition to substantially diminishing the computational load, provides sound starting values that allow the algorithm to reach convergence faster. We also focus on the model selection problem. We test our algorithm using synthetic and real databases, and find several advantages, when compared to other standard existing procedures.
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    Indoor Mobile Robotics at Grima, PUC
    (2012) Caro, Luis; Correa, Javier; Espinace, Pablo; Langdon, Daniel; Maturana, Daniel; Mitnik, Ruben; Montabone, Sebastian; Pszczolkowski, Stefan; Araneda, Anita; Mery Quiroz, Domingo Arturo; Torres, Miguel; Soto, Alvaro