| 김효원 교수_ Set-Type Belief Propagation With Applications to Poisson Multi-Bernoulli SLAM | |||||
| 분류 | 논문 | 작성자 | 미래국방지능형ICT교육연구단 | ||
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| 조회수 | 4 | 등록일 | 2025.03.10 | ||
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EEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 72, 2024 Set-Type Belief Propagation With Applications to Poisson Multi-Bernoulli SLAM Hyowon Kim , Ángel F. García-Fernández Yuxuan Xia , Lennart Svensson ,YuGe , and Henk Wymeersch Abstract Belief propagation (BP) is a useful probabilistic in ference algorithm for efficiently computing approximate marginal probability densities of random variables. However, in its stan dard 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. 10.1109/TSP.2024.3383543
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