  We aim for mobile robots to function in a variety of common human
environments. Such robots need to be able to reason about the locations of
previously unseen target objects. Landmark objects can help this reasoning by
narrowing down the search space significantly. More specifically, we can
exploit background knowledge about common spatial relations between landmark
and target objects. For example, seeing a table and knowing that cups can often
be found on tables aids the discovery of a cup. Such correlations can be
expressed as distributions over possible pairing relationships of objects. In
this paper, we propose an active visual object search strategy method through
our introduction of the Semantic Linking Maps (SLiM) model. SLiM simultaneously
maintains the belief over a target object's location as well as landmark
objects' locations, while accounting for probabilistic inter-object spatial
relations. Based on SLiM, we describe a hybrid search strategy that selects the
next best view pose for searching for the target object based on the maintained
belief. We demonstrate the efficiency of our SLiM-based search strategy through
comparative experiments in simulated environments. We further demonstrate the
real-world applicability of SLiM-based search in scenarios with a Fetch mobile
manipulation robot.
