Poisson Multi-Bernoulli Mapping Using Gibbs Sampling
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| Názov: | Poisson Multi-Bernoulli Mapping Using Gibbs Sampling |
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| Autori: | 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 |
| Predmety: | 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 súboru: | electronic |
| Prístupová URL adresa: | https://research.chalmers.se/publication/249608 https://research.chalmers.se/publication/249608/file/249608_Fulltext.pdf |
| Databáza: | SwePub |
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Nájsť tento článok vo Web of Science | Abstrakt: | 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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| ISSN: | 19410476 1053587X |
| DOI: | 10.1109/tsp.2017.2675866 |