Simultaneous Localisation and Mapping in Dynamic Environments (SLAMIDE) with Reversible Data Associa

The conventional technique for dealing with dynamic objects in SLAM is to detect them and then either treat them as outliers We propose a technique that combines the least-squares formulation of SLAM and sliding window optimisation together with generalised expectation maximisation, to incorporate both dynamic and stationary objects directly into SLAM estimation. The sliding window allows us to postpone the commitment of model selection and data association decisions by delaying when they are marginalised permanently into the estimate. The two main contributions of this paper are thus: (i) using reversible model selection to include dynamic objects into SLAM and (ii) incorporating reversible data association. We show empirically that (i) if dynamic objects are present our method can include them in a single framework and hence maintain a consistent estimate and (ii) our estimator remains consistent when data association is difficult, for instance in the presence of clutter. We summarise the results of detailed and extensive tests of our method against various benchmark algorithms, showing its effectiveness.