Poisson Multi-Bernoulli Mapping Using Gibbs Sampling
Artikel i vetenskaplig tidskrift, 2017

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.

Monte Carlo methods

inference algorithms

extended object

sampling methods

Statistical mapping

Författare

Maryam Fatemi

Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Karl Granström

Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Lennart Svensson

Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

F. J. R. Ruiz

Columbia University in the City of New York

University of Cambridge

Lars Hammarstrand

Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

IEEE Transactions on Signal Processing

1053-587X (ISSN)

Vol. 65 2814-2827 7867064

Ämneskategorier

Signalbehandling

DOI

10.1109/tsp.2017.2675866