In a recent French robotic contest, the objective was to develop a multi-robot system able to autonomously map and explore an unknown area while also detecting and localizing objects in the environment. The scientific issues of this project deal with SLAM (Simultaneous Localization and Mapping), object recognition and multi-robot collaboration for the exploration. As a participant in this challenge, we proposed a new decentralized Markov decision process (Dec-MDP) resolution based on distributed value functions (DVF) to compute multi-robot exploration strategies. The idea is to take advantage of sparse interactions by allowing each robot to calculate locally a strategy that maximizes the explored space while minimizing robots interactions. In this paper, we will present an adaptation of this method to improve also object recognition. It consists in integrating in the DVF the interest to cover explored areas with photos. Robots will then act to maximize the explored space and the photo coverage ensuring a better perception and object recognition. After a brief description of the robotic contest and our system, we will present our Dec-MDP resolution based on DVF and its application to multi-robot exploration. The next part of the paper will be dedicated to the use of DVF to explore and perceive with experimental results from simulated scenarios.