Poisson Multi-Bernoulli Mapping Using Gibbs Sampling
Journal article, 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.

Statistical mapping

extended object

Monte Carlo methods

inference algorithms

sampling methods

Author

Maryam Fatemi

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Karl Granström

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Lennart Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

F. J. R. Ruiz

Columbia University in the City of New York

University of Cambridge

Lars Hammarstrand

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

IEEE Transactions on Signal Processing

1053-587X (ISSN)

Vol. 65 11 2814-2827 7867064

COPPLAR CampusShuttle cooperative perception & planning platform

VINNOVA, 2016-01-01 -- 2018-12-31.

Subject Categories

Signal Processing

DOI

10.1109/tsp.2017.2675866

More information

Latest update

4/11/2019