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Poisson Multi-Bernoulli Mapping Using Gibbs Sampling

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Název: Poisson Multi-Bernoulli Mapping Using Gibbs Sampling
Autoři: Fatemi, Maryam, 1982, Granström, Karl, 1981, Svensson, Lennart, 1976, Ruiz, F. J. R., Hammarstrand, Lars, 1979
Zdroj: COPPLAR CampusShuttle cooperative perception & planning platform IEEE Transactions on Signal Processing. 65(11):2814-2827
Témata: Statistical mapping, extended object, Monte Carlo methods, inference algorithms, sampling methods
Popis: This paper addresses the mapping problem. Using a conjugate prior form, we derive the exact theoretical batch multiobject posterior density of the map given a set of measurements. The landmarks in the map are modeled as extended objects, and the measurements are described as a Poisson process, conditioned on the map. We use a Poisson process prior on the map and prove that the posterior distribution is a hybrid Poisson, multi-Bernoulli mixture distribution. We devise a Gibbs sampling algorithm to sample from the batch multiobject posterior. The proposed method can handle uncertainties in the data associations and the cardinality of the set of landmarks, and is parallelizable, making it suitable for large-scale problems. The performance of the proposed method is evaluated on synthetic data and is shown to outperform a state-of-the-art method.
Popis souboru: electronic
Přístupová URL adresa: https://research.chalmers.se/publication/249608
https://research.chalmers.se/publication/249608/file/249608_Fulltext.pdf
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  Label: Title
  Group: Ti
  Data: Poisson Multi-Bernoulli Mapping Using Gibbs Sampling
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Fatemi%2C+Maryam%22">Fatemi, Maryam</searchLink>, 1982<br /><searchLink fieldCode="AR" term="%22Granström%2C+Karl%22">Granström, Karl</searchLink>, 1981<br /><searchLink fieldCode="AR" term="%22Svensson%2C+Lennart%22">Svensson, Lennart</searchLink>, 1976<br /><searchLink fieldCode="AR" term="%22Ruiz%2C+F%2E+J%2E+R%2E%22">Ruiz, F. J. R.</searchLink><br /><searchLink fieldCode="AR" term="%22Hammarstrand%2C+Lars%22">Hammarstrand, Lars</searchLink>, 1979
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  Data: <i>COPPLAR CampusShuttle cooperative perception & planning platform IEEE Transactions on Signal Processing</i>. 65(11):2814-2827
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  Data: <searchLink fieldCode="DE" term="%22Statistical+mapping%22">Statistical mapping</searchLink><br /><searchLink fieldCode="DE" term="%22extended+object%22">extended object</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+methods%22">Monte Carlo methods</searchLink><br /><searchLink fieldCode="DE" term="%22inference+algorithms%22">inference algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22sampling+methods%22">sampling methods</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: This paper addresses the mapping problem. Using a conjugate prior form, we derive the exact theoretical batch multiobject posterior density of the map given a set of measurements. The landmarks in the map are modeled as extended objects, and the measurements are described as a Poisson process, conditioned on the map. We use a Poisson process prior on the map and prove that the posterior distribution is a hybrid Poisson, multi-Bernoulli mixture distribution. We devise a Gibbs sampling algorithm to sample from the batch multiobject posterior. The proposed method can handle uncertainties in the data associations and the cardinality of the set of landmarks, and is parallelizable, making it suitable for large-scale problems. The performance of the proposed method is evaluated on synthetic data and is shown to outperform a state-of-the-art method.
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        Value: 10.1109/tsp.2017.2675866
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      – Text: English
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      – SubjectFull: Statistical mapping
        Type: general
      – SubjectFull: extended object
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      – SubjectFull: Monte Carlo methods
        Type: general
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            NameFull: Fatemi, Maryam
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            NameFull: Granström, Karl
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            NameFull: Hammarstrand, Lars
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              Y: 2017
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