  To accomplish tasks in human-centric indoor environments, robots need to
represent and understand the world in terms of objects and their attributes. We
refer to this attribute-based representation as a world model, and consider how
to acquire it via noisy perception and maintain it over time, as objects are
added, changed, and removed in the world. Previous work has framed this as
multiple-target tracking problem, where objects are potentially in motion at
all times. Although this approach is general, it is computationally expensive.
We argue that such generality is not needed in typical world modeling tasks,
where objects only change state occasionally. More efficient approaches are
enabled by restricting ourselves to such semi-static environments.
  We consider a previously-proposed clustering-based world modeling approach
that assumed static environments, and extend it to semi-static domains by
applying a dependent Dirichlet-process (DDP) mixture model. We derive a novel
MAP inference algorithm under this model, subject to data association
constraints. We demonstrate our approach improves computational performance in
semi-static environments.
