CUED Publications database

Poisson Multi-Bernoulli Mapping Using Gibbs Sampling

Fatemi, M and Granström, K and Svensson, L and Ruiz, FJR and Hammarstrand, L (2017) Poisson Multi-Bernoulli Mapping Using Gibbs Sampling. IEEE Transactions on Signal Processing, 65. pp. 2814-2827. ISSN 1053-587X

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Abstract

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.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 29 Oct 2018 20:08
Last Modified: 10 Apr 2021 01:08
DOI: 10.1109/TSP.2017.2675866