  This paper addresses the topic of semantic world modeling by conjoining
probabilistic reasoning and object anchoring. The proposed approach uses a
so-called bottom-up object anchoring method that relies on the rich continuous
data from perceptual sensor data. A novel anchoring matching function method
learns to maintain object entities in space and time and is validated using a
large set of trained humanly annotated ground truth data of real-world objects.
For more complex scenarios, a high-level probabilistic object tracker has been
integrated with the anchoring framework and handles the tracking of occluded
objects via reasoning about the state of unobserved objects. We demonstrate the
performance of our integrated approach through scenarios such as the shell game
scenario, where we illustrate how anchored objects are retained by preserving
relations through probabilistic reasoning.
