The Labeled Multi-Bernoulli SLAM Filter

Author

Deusch, Hendrik ; Reuter, Stephan ; Dietmayer, Klaus

Author_Institution

Inst. of Meas., Control & Microtechnol., Ulm Univ., Ulm, Germany

Volume

22

Issue

10

fYear

2015

fDate

Oct. 2015

Firstpage

1561

Lastpage

1565

Abstract

In this contribution, a new algorithm addressing the simultaneous localization and mapping (SLAM) problem is proposed: a Rao-Blackwellized implementation of the Labeled Multi-Bernoulli SLAM (LMB-SLAM) filter. Further, we establish that the LMB-SLAM does not require the approximations used in Probability Hypothesis Density SLAM (PHD-SLAM). The LMB-SLAM is shown to outperform PHD-SLAM in simulations by providing a more accurate map as well as an improved estimate of the vehicle´s trajectory which is an expected result due to the superior performance of the LMB filter in tracking applications.

Keywords

Kalman filters; SLAM (robots); pose estimation; set theory; LMB-SLAM filter; Rao-Blackwellized implementation; labeled multiBernoulli SLAM filter; simultaneous localization and mapping problem; tracking applications; vehicle trajectory estimation; Approximation methods; Distribution functions; Graphical models; Simultaneous localization and mapping; Trajectory; Vectors; Vehicles; Labeled multi-bernoulli; SLAM; localization; mapping; random finite sets;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2015.2414274

Filename

7062004