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

Authors :
Kim, Hyowon
Garcia-Fernandez, Angel F.
Ge, Yu
Xia, Yuxuan
Svensson, Lennart
Wymeersch, Henk
Source :
IEEE Transactions on Signal Processing; 2024, Vol. 72 Issue: 1 p1989-2005, 17p
Publication Year :
2024

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.

Details

Language :
English
ISSN :
1053587X
Volume :
72
Issue :
1
Database :
Supplemental Index
Journal :
IEEE Transactions on Signal Processing
Publication Type :
Periodical
Accession number :
ejs66172451
Full Text :
https://doi.org/10.1109/TSP.2024.3383543