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To accomplish tasks in human-centric indoor environments, robots need torepresent and understand the world in terms of objects and their attributes. Werefer to this attribute-based representation as a world model, and consider howto acquire it via noisy perception and maintain it over time, as objects areadded, changed, and removed in the world. Previous work has framed this asmultiple-target tracking problem, where objects are potentially in motion atall 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 areenabled by restricting ourselves to such semi-static environments.  We consider a previously-proposed clustering-based world modeling approachthat assumed static environments, and extend it to semi-static domains byapplying a dependent Dirichlet-process (DDP) mixture model. We derive a novelMAP inference algorithm under this model, subject to data associationconstraints. We demonstrate our approach improves computational performance insemi-static environments.

Object-based World Modeling in Semi-Static Environments with Dependent Dirichlet-Process Mixtures