A Multi-Hypotheses Importance Density for SLAM in Cluttered Scenarios

Ossi Kaltiokallio, Roland Hostettler, Yu Ge, Hyowon Kim, Jukka Talvitie, Henk Wymeersch, Mikko Valkama

Tutkimustuotos: ArtikkeliScientificvertaisarvioitu

10 Lataukset (Pure)

Abstrakti

One of the most fundamental problems in simultaneous localization and mapping (SLAM) is the ability to take into account data association (DA) uncertainties. In this paper, this problem is addressed by proposing a multi-hypotheses sampling distribution for particle filtering-based SLAM algorithms. By modeling the measurements and landmarks as random finite sets, an importance density approximation that incorporates DA uncertainties is derived. Then, a tractable Gaussian mixture model approximation of the multi-hypotheses importance density is proposed in which each mixture component represents a different DA. Finally, an iterative method for approximating the mixture components of the sampling distribution is utilized and a partitioned update strategy is developed. Using synthetic and experimental data, it is demonstrated that the proposed importance density improves the accuracy and robustness of landmark-based SLAM in cluttered scenarios over state-of-the-art methods. At the same time, the partitioned update strategy makes it possible to include multiple DA hypotheses in the importance density approximation, leading to a favorable linear complexity scaling, in terms of the number of landmarks in the field-of-view.

AlkuperäiskieliEnglanti
Sivut1019-1035
JulkaisuIEEE Transactions on Robotics
Vuosikerta40
Varhainen verkossa julkaisun päivämäärä4 jouluk. 2023
DOI - pysyväislinkit
TilaJulkaistu - 2024
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Julkaisufoorumi-taso

  • Jufo-taso 3

!!ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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