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Batch SLAM with PMBM Data Association Sampling and Graph-Based Optimization

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Titel: Batch SLAM with PMBM Data Association Sampling and Graph-Based Optimization
Autoren: Ge, Yu, 1995, Kaltiokallio, Ossi, Xia, Yuxuan, 1993, Garcia, Angel, 1984, Kim, Hyowon, Talvitie, Jukka, Valkama, M., Wymeersch, Henk, 1976, Svensson, Lennart, 1976
Quelle: IEEE Transactions on Signal Processing. 73:2139-2153
Schlagwörter: sampling, RFS, DA, SLAM, Batch processing, graph-based SLAM, PMBM, correlation
Beschreibung: 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.
Dateibeschreibung: electronic
Zugangs-URL: https://research.chalmers.se/publication/546535
https://research.chalmers.se/publication/546535/file/546535_Fulltext.pdf
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  Data: Batch SLAM with PMBM Data Association Sampling and Graph-Based Optimization
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Ge%2C+Yu%22">Ge, Yu</searchLink>, 1995<br /><searchLink fieldCode="AR" term="%22Kaltiokallio%2C+Ossi%22">Kaltiokallio, Ossi</searchLink><br /><searchLink fieldCode="AR" term="%22Xia%2C+Yuxuan%22">Xia, Yuxuan</searchLink>, 1993<br /><searchLink fieldCode="AR" term="%22Garcia%2C+Angel%22">Garcia, Angel</searchLink>, 1984<br /><searchLink fieldCode="AR" term="%22Kim%2C+Hyowon%22">Kim, Hyowon</searchLink><br /><searchLink fieldCode="AR" term="%22Talvitie%2C+Jukka%22">Talvitie, Jukka</searchLink><br /><searchLink fieldCode="AR" term="%22Valkama%2C+M%2E%22">Valkama, M.</searchLink><br /><searchLink fieldCode="AR" term="%22Wymeersch%2C+Henk%22">Wymeersch, Henk</searchLink>, 1976<br /><searchLink fieldCode="AR" term="%22Svensson%2C+Lennart%22">Svensson, Lennart</searchLink>, 1976
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  Data: <i>IEEE Transactions on Signal Processing</i>. 73:2139-2153
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22sampling%22">sampling</searchLink><br /><searchLink fieldCode="DE" term="%22RFS%22">RFS</searchLink><br /><searchLink fieldCode="DE" term="%22DA%22">DA</searchLink><br /><searchLink fieldCode="DE" term="%22SLAM%22">SLAM</searchLink><br /><searchLink fieldCode="DE" term="%22Batch+processing%22">Batch processing</searchLink><br /><searchLink fieldCode="DE" term="%22graph-based+SLAM%22">graph-based SLAM</searchLink><br /><searchLink fieldCode="DE" term="%22PMBM%22">PMBM</searchLink><br /><searchLink fieldCode="DE" term="%22correlation%22">correlation</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: 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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      – Type: doi
        Value: 10.1109/TSP.2025.3567916
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      – Text: English
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        PageCount: 15
        StartPage: 2139
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      – SubjectFull: sampling
        Type: general
      – SubjectFull: RFS
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      – SubjectFull: SLAM
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      – SubjectFull: Batch processing
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      – SubjectFull: graph-based SLAM
        Type: general
      – SubjectFull: PMBM
        Type: general
      – SubjectFull: correlation
        Type: general
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      – TitleFull: Batch SLAM with PMBM Data Association Sampling and Graph-Based Optimization
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              M: 01
              Type: published
              Y: 2025
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