This article proposes a unique active relative localization mechanism for multi-agent simultaneous localization and mapping, in which an agent to be observed is considered as a task, and the others who want to assist that agent will perform that task by relative observation. A task allocation algorithm based on deep reinforcement learning is proposed for this mechanism. Each agent can choose whether to localize other agents or to continue independent simultaneous localization and mapping on its own initiative. By this way, the process of each agent simultaneous localization and mapping will be interacted by the collaboration. Firstly, a unique observation function which models the whole multi-agent system is obtained based on ORBSLAM. Secondly, a novel type of Deep Q Network called multi-agent systemDeep Q Network (MAS-DQN) is deployed to learn correspondence between Q value and state–action pair, abstract representation of agents in multi-agent system is learned in the process of collaboration among agents. Finally, each agent must act with a certain degree of freedom according to MAS-DQN. The simulation results of comparative experiments prove that this mechanism improves the efficiency of cooperation in the process of multi-agent simultaneous localization and mapping.

中文翻译：

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基于Deep Q Network的多智能体视觉同时定位与建图相对观察中的主动协作

本文提出了一种独特的多智能体同时定位和映射的主动相对定位机制，其中一个被观察的智能体被视为一个任务，其他想要帮助该智能体的人将通过相对观察来执行该任务。针对该机制提出了一种基于深度强化学习的任务分配算法。每个智能体可以选择是定位其他智能体还是自主地继续独立的同步定位和映射。通过这种方式，每个代理同时定位和映射的过程将通过协作进行交互。首先，基于 ORBSLAM 获得对整个多智能体系统建模的独特观察函数。第二，一种称为多智能体系统的新型深度 Q 网络部署了深度 Q 网络（MAS-DQN）来学习 Q 值和状态-动作对之间的对应关系，在多智能体系统之间的协作过程中学习智能体的抽象表示代理。最后，每个代理必须根据 MA​​S-DQN 以一定的自由度行动。对比实验的仿真结果证明，该机制提高了多智能体同时定位和建图过程中的协作效率。