Interest-point-based landmark computation for agents’ spatial description coordination

In applications involving multiple conversational agents, each of these agents has its own view of a visual scene, and thus all the agents must establish common visual landmarks in order to coordinate their space understanding and to coherently share generated spatial descriptions of this scene. Whereas natural language processing approaches contribute to define the common ground through dialogues between these agents, we propose in this paper a computer-vision system to determine the object of reference for both agents efficiently and automatically. Our approach consists in processing each agent's view by computing the related, visual interest points, and then by matching them in order to extract the salient and meaningful landmark. Our approach has been successfully tested on real-world data, and its performance and design allow its use for embedded robotic system communication.