Batch SLAM with PMBM Data Association Sampling and Graph-Based Optimization
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| Název: | Batch SLAM with PMBM Data Association Sampling and Graph-Based Optimization |
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| Autoři: | Ge, Yu, 1995, Kaltiokallio, Ossi, Xia, Yuxuan, 1993, Garcia, Angel, 1984, Kim, Hyowon, Talvitie, Jukka, Valkama, M., Wymeersch, Henk, 1976, Svensson, Lennart, 1976 |
| Zdroj: | IEEE Transactions on Signal Processing. 73:2139-2153 |
| Témata: | sampling, RFS, DA, SLAM, Batch processing, graph-based SLAM, PMBM, correlation |
| Popis: | Simultaneous localization and mapping (SLAM) methods need to both solve the data association (DA) problem and the joint estimation of the sensor trajectory and the map, conditioned on a DA. In this paper, we propose a novel integrated approach to solve both the DA problem and the batch SLAM problem simultaneously, combining random finite set (RFS) theory and the graph-based SLAM approach. A sampling method based on the Poisson multi-Bernoulli mixture (PMBM) density is designed for dealing with the DA uncertainty, and a graph-based SLAM solver is applied for the conditional SLAM problem. In the end, a post-processing approach is applied to merge SLAM results from different iterations. Using synthetic data, it is demonstrated that the proposed SLAM approach achieves performance close to the posterior Cramér-Rao bound, and outperforms state-of-the-art RFS-based SLAM filters in high clutter and high process noise scenarios. |
| Popis souboru: | electronic |
| Přístupová URL adresa: | https://research.chalmers.se/publication/546535 https://research.chalmers.se/publication/546535/file/546535_Fulltext.pdf |
| Databáze: | SwePub |
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Nájsť tento článok vo Web of Science | Abstrakt: | Simultaneous localization and mapping (SLAM) methods need to both solve the data association (DA) problem and the joint estimation of the sensor trajectory and the map, conditioned on a DA. In this paper, we propose a novel integrated approach to solve both the DA problem and the batch SLAM problem simultaneously, combining random finite set (RFS) theory and the graph-based SLAM approach. A sampling method based on the Poisson multi-Bernoulli mixture (PMBM) density is designed for dealing with the DA uncertainty, and a graph-based SLAM solver is applied for the conditional SLAM problem. In the end, a post-processing approach is applied to merge SLAM results from different iterations. Using synthetic data, it is demonstrated that the proposed SLAM approach achieves performance close to the posterior Cramér-Rao bound, and outperforms state-of-the-art RFS-based SLAM filters in high clutter and high process noise scenarios. |
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| ISSN: | 19410476 1053587X |
| DOI: | 10.1109/TSP.2025.3567916 |