  We present a filtering-based method for semantic mapping to simultaneously
detect objects and localize their 6 degree-of-freedom pose. For our method,
called Contextual Temporal Mapping (or CT-Map), we represent the semantic map
as a belief over object classes and poses across an observed scene. Inference
for the semantic mapping problem is then modeled in the form of a Conditional
Random Field (CRF). CT-Map is a CRF that considers two forms of relationship
potentials to account for contextual relations between objects and temporal
consistency of object poses, as well as a measurement potential on
observations. A particle filtering algorithm is then proposed to perform
inference in the CT-Map model. We demonstrate the efficacy of the CT-Map method
with a Michigan Progress Fetch robot equipped with a RGB-D sensor. Our results
demonstrate that the particle filtering based inference of CT-Map provides
improved object detection and pose estimation with respect to baseline methods
that treat observations as independent samples of a scene.
