Poisson Multi-Bernoulli Mapping Using Gibbs Sampling

Maryam Fatemi; Karl Granström; Lennart Svensson; Francisco J. R. Ruiz; Lars Hammarstrand

doi:10.1109/tsp.2017.2675866

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arXiv · 1811.03154

Poisson Multi-Bernoulli Mapping Using Gibbs Sampling

Maryam Fatemi·Karl Granström·Lennart Svensson·Francisco J. R. Ruiz·Lars Hammarstrand

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Abstract

This paper addresses the mapping problem. Using a conjugate prior form, we derive the exact theoretical batch multi-object 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 multi-object 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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BibTeX RIS

Maryam Fatemi, Karl Granström, Lennart Svensson, Francisco J. R. Ruiz, Lars Hammarstrand. 2018-11-07. Poisson Multi-Bernoulli Mapping Using Gibbs Sampling. https://doi.org/10.1109/tsp.2017.2675866

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