Set-Type Belief Propagation With Applications to Poisson Multi-Bernoulli SLAM

Kim, Hyowon ORCID: 0000-0002-0473-1471, García-Fernández, Ángel F ORCID: 0000-0002-6471-8455, Ge, Yu ORCID: 0000-0003-1747-2664, Xia, Yuxuan ORCID: 0000-0002-2788-7911, Svensson, Lennart ORCID: 0000-0003-0206-9186 and Wymeersch, Henk ORCID: 0000-0002-1298-6159 (2024) Set-Type Belief Propagation With Applications to Poisson Multi-Bernoulli SLAM. IEEE Transactions on Signal Processing, 72 (99). pp. 1989-2005.

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Abstract

Belief propagation (BP) is a useful probabilistic inference algorithm for efficiently computing approximate marginal probability densities of random variables. However, in its standard form, BP is only applicable to the vector-type random variables with a fixed and known number of vector elements, while certain applications rely on random finite sets (RFSs) with an unknown number of vector elements. In this paper, we develop BP rules for factor graphs defined on sequences of RFSs where each RFS has an unknown number of elements, with the intention of deriving novel inference methods for RFSs. Furthermore, we show that vector-type BP is a special case of set-type BP, where each RFS follows the Bernoulli process. To demonstrate the validity of developed set-type BP, we apply it to the Poisson multi-Bernoulli (PMB) filter for simultaneous localization and mapping (SLAM), which naturally leads to a set-type BP PMB-SLAM method, which is analogous to a vector type SLAM method, subject to minor modifications.

Item Type:	Article
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	02 Apr 2024 16:27
Last Modified:	07 May 2024 11:03
DOI:	10.1109/tsp.2024.3383543
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3180014