Despite the great development of multirobot technologies, efficiently and collaboratively exploring an unknown environment is still a big challenge. In this paper, we propose AIM-Mapping, a Asymmetric InforMation Enhanced Mapping framework. The framework fully utilizes the privilege information in the training process to help construct the environment representation as well as the supervised signal in an asymmetric actor-critic training framework. Specifically, privilege information is used to evaluate the exploration performance through an asymmetric feature representation module and a mutual information evaluation module. The decision-making network uses the trained feature encoder to extract structure information from the environment and combines it with a topological map constructed based on geometric distance. Utilizing this kind of topological map representation, we employ topological graph matching to assign corresponding boundary points to each robot as long-term goal points. We conduct experiments in real-world-like scenarios using the Gibson simulation environments. It validates that the proposed method, when compared to existing methods, achieves great performance improvement.

翻译：尽管多机器人技术已取得显著进展，在未知环境中实现高效协同探索仍面临巨大挑战。本文提出AIM-Mapping——一种非对称信息增强建图框架。该框架在训练过程中充分利用特权信息，通过非对称行动者-评论家训练架构构建环境表征与监督信号。具体而言，特权信息通过非对称特征表征模块与互信息评估模块实现对探索性能的量化评估。决策网络使用训练后的特征编码器从环境中提取结构信息，并将其与基于几何距离构建的拓扑地图相结合。利用此类拓扑地图表征，我们采用拓扑图匹配方法为每个机器人分配对应的边界点作为长期目标点。我们在Gibson仿真环境中开展类真实场景实验，验证了所提方法相较于现有方法能实现显著的性能提升。