For a multi-robot team that collaboratively explores an unknown environment, it is of vital importance that collected information is efficiently shared among robots in order to support exploration and navigation tasks. Practical constraints of wireless channels, such as limited bandwidth, urge robots to carefully select information to be transmitted. In this paper, we consider the case where environmental information is modeled using a 3D Scene Graph, a hierarchical map representation that describes both geometric and semantic aspects of the environment. Then, we leverage graph-theoretic tools, namely graph spanners, to design greedy algorithms that efficiently compress 3D Scene Graphs with the aim of enabling communication between robots under bandwidth constraints. Our compression algorithms are navigation-oriented in that they are designed to approximately preserve shortest paths between locations of interest, while meeting a user-specified communication budget constraint. The effectiveness of the proposed algorithms is demonstrated in synthetic robot navigation experiments in a realistic simulator. A video abstract is available at https://youtu.be/nKYXU5VC6A8.

翻译：对于协同探索未知环境的多机器人团队而言，高效共享所采集的信息以支持探索与导航任务至关重要。无线信道的实际约束（如有限带宽）要求机器人谨慎选择待传输的信息。本文考虑使用3D场景图对环境信息进行建模——这是一种同时描述环境几何与语义属性的分层地图表示。进而，我们利用图论工具（即图稀疏化）设计贪婪算法，在带宽约束下高效压缩3D场景图以实现机器人间的通信。所提出的压缩算法面向导航任务，旨在近似保持兴趣位置间的最短路径，同时满足用户指定的通信预算约束。在逼真模拟器中进行的合成机器人导航实验验证了所提算法的有效性。视频摘要见https://youtu.be/nKYXU5VC6A8。