The Labeled Multi-Bernoulli SLAM Filter

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.