Lab Meeting 9 July (Leo): Simultaneous Localisation and Mapping in Dynamic Environments (SLAMIDE) with Reversible Data Association

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

authors:
Charles Bibby, Ian Reid

from:
RSS 07

Abstract:
The conventional technique for dealing with dynamic
objects in SLAM is to detect them and then either treat
them as outliers [20][1] or track them separately using traditional
multi-target tracking [18]. 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.