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This paper proposes a unique active relative localization mechanism formulti-agent Simultaneous Localization and Mapping(SLAM),in which a agent to beobserved are considered as a task, which is performed by others assisting thatagent by relative observation. A task allocation algorithm based on deepreinforcement learning are proposed for this mechanism. Each agent can choosewhether to localize other agents or to continue independent SLAM on it owninitiative. By this way, the process of each agent SLAM will be interacted bythe collaboration. Firstly, based on the characteristics of ORBSLAM, a uniqueobservation function which models the whole MAS is obtained. Secondly, a noveltype of Deep Q network(DQN) called MAS-DQN is deployed to learn correspondencebetween Q Value and state-action pair,abstract representation of agents in MASare learned in the process of collaboration among agents. Finally, each agentmust act with a certain degree of freedom according to MAS-DQN. The simulationresults of comparative experiments prove that this mechanism improves theefficiency of cooperation in the process of multi-agent SLAM.

Active collaboration in relative observation for Multi-agent visual SLAM based on Deep Q Network