Abstract-In this paper, our goal is to search for a novel object, where we have a prior map of the environment and knowledge of some of the objects in it, but no information about the location of the specific novel object. We develop a probabilistic model over possible object locations that utilizes objectobject and object-scene context. This model can be queried for any of over 25,000 naturally occurring objects in the world and is trained from labeled data acquired from the captions of photos on the Flickr website. We show that these simple models based on object co-occurrences perform surprisingly well at localizing arbitrary objects in an office setting. In addition, we show how to compute paths that minimize the expected distance to the query object and show that this approach performs better than a greedy approach. Finally, we give preliminary results for grounding our approach in object classifiers.
In REF a method for finding novel objects in the scene is proposed which utilizes the given information about the known objects including and the map of the environment.
Utilizing object-object and object-scene context when planning to find things
2009 IEEE International Conference on Robotics and Automation
2009 IEEE International Conference on Robotics and Automation
Engineering Computer Science