PROJECT TITLE :

The Labeled Multi-Bernoulli SLAM Filter

ABSTRACT:

In this contribution, a brand new algorithm addressing the simultaneous localization and mapping (SLAM) downside is proposed: a Rao-Blackwellized implementation of the Labeled Multi-Bernoulli SLAM (LMB-SLAM) filter. Further, we establish that the LMB-SLAM does not need the approximations utilized in Likelihood Hypothesis Density SLAM (PHD-SLAM). The LMB-SLAM is shown to outperform PHD-SLAM in simulations by providing a a lot of accurate map along with an improved estimate of the vehicle’s trajectory which is an expected result because of the superior performance of the LMB filter in tracking applications.


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