Searching for Objects in Human Living Environments based on Relevant Inferred and Mined Priors

Service robots performing tasks in human environments constantly face changes due to the dynamic of the environments. Such robots need to reason about their surrounding for a better understanding of it. Besides, it is important to demonstrate capabilities that potential users would find useful, thus validating the development of such systems. One of these capabilities is to help a person to find what she or he is looking for. This mundane task of searching for an object is highly relevant in showing the non-expert user that a robot can understand the world. In this paper, we propose an efficient search strategy to find target objects that have not been seen before, based on the reasoning about in which scenes and with which objects they co-occur. Our method consists of an inference process based on a Conditional Random Field (CRF), that fuses the information about other previously detected objects, the semantic floor map, and the object-object/-room relations, to build a prediction map with the most promising locations for an unseen object. To validate our work, comparative experiments in simulated environments have been performed, demonstrating the efficiency of our proposed search strategy.