Prior-assisted propagation of spatial information for object search

We propose a novel method for object search in realistic environments. We formalize object search as a probabilistic inference problem over possible object locations. The method makes two contributions. First, we identify five priors, each capturing structure inherent to the physical world that is relevant to the search problem. Second, we propose a formalization of the object search problem that leverages these priors. Our formalization in form of a probabilistic graphical model is capable of combining the various sources of information into a consistent probability distribution over object locations. The formalization allows us to sharpen the distribution by propagating the knowledge across locations. We employ the reasoning method to select actions of a searching robot in a simulated environment and show that it results in more efficient object search.