This paper presents an hybrid metric-topological mapping for multi-robot observation of a human scene. The scene is defined as a set of body joints. Mobile robots have to cooperate to find a position around the scene that maximizes the number of observed joints. It is assumed that the robots can communicate but have no map of the environment. The map is updated cooperatively by exchanging only high-level data, thereby reducing the communication payload. The mapping is also realized in an incremental way to explore promising areas of the environment while keeping state-space complexity reasonable. We proposed an on-line distributed heuristic search combined to this hybrid mapping. We showed the efficiency of the approach on a fleet of three real robots, in particular its ability to quickly explore and find the team position maximizing the joint observation quality.