This work proposes an autonomous multi-robot exploration pipeline that coordinates the behaviors of robots in an indoor environment composed of multiple rooms. Contrary to simple frontier-based exploration approaches, we aim to enable robots to methodically explore and observe an unknown set of rooms in a structured building, keeping track of which rooms are already explored and sharing this information among robots to coordinate their behaviors in a distributed manner. To this end, we propose (1) a geometric cue extraction method that processes 3D point cloud data and detects the locations of potential cues such as doors and rooms, (2) a circular decomposition for free spaces used for target assignment. Using these two components, our pipeline effectively assigns tasks among robots, and enables a methodical exploration of rooms. We evaluate the performance of our pipeline using a team of up to 3 aerial robots, and show that our method outperforms the baseline by 33.4% in simulation and 26.4% in real-world experiments.

翻译：本文提出了一种自主多机器人探索框架，用于协调多个机器人在由多房间构成的室内环境中的行为。与基于简单前沿的探索方法不同，我们旨在使机器人能够对结构化建筑中未知的房间集合进行系统化探索与观测，实时追踪已探索房间的状态，并通过分布式方式在机器人间共享该信息以协调其行为。为此，我们提出：(1) 一种几何线索提取方法，通过处理3D点云数据检测门、房间等潜在线索的位置；(2) 一种用于目标分配的自由空间圆形分解方法。基于这两个组件，我们的框架能有效分配机器人间的任务，实现房间的系统化探索。我们使用最多3台空中机器人组成的集群评估了框架性能，结果表明：我们的方法在仿真环境中相比基线方法性能提升33.4%，在真实场景实验中提升26.4%。