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 to enable communication 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:对于一个合作性地探索未知环境的多机器人团队,高效地分享所收集的信息对于支持探索和导航任务至关重要。无线通信通道的实际约束,如带宽有限,迫使机器人仔细选择要传输的信息。在本文中,我们考虑将环境信息建模为三维场景图,这是一种描述环境几何和语义方面的分层映射表现形式。然后,我们利用图论工具,即图跨度,设计贪心算法,以便能够在带宽约束下有效压缩三维场景图来进行通信。我们的压缩算法是导航导向的,即它们旨在大致保留感兴趣位置之间的最短路径,同时满足用户指定的通信预算约束。本文中提出的算法的有效性在实际模拟器中的合成机器人导航实验中得到了证明。 视频摘要可在 https://youtu.be/nKYXU5VC6A8 上查看。