Semantic world modeling using probabilistic multiple hypothesis anchoring

In order to successfully perform typical household tasks such as manipulation or navigation, domestic robots need an accurate description of the world they are operating in. Creating and maintaining such a description, in this work referred to as world model, is a non-trivial task in a domestic environment that typically has a high number of objects, and is unstructured and dynamically changing. This work introduces probabilistic multiple hypothesis anchoring to create and maintain a semantically rich world model using probabilistic anchoring. Multiple hypothesis tracking-based data association is included to be able to deal with ambiguous scenarios. Multiple model tracking is included to be able to easily incorporate different kinds of prior knowledge.

► We introduce probabilistic multiple hypothesis anchoring. ► Anchoring with multiple model tracking and multiple hypothesis based data association. ► Experimental validation of the approach in a series of challenging experiments. ► An analysis of the (im)possibilities of the method based on the assumptions made.