The most common way for robots to handle environmental information is by using maps. At present, each kind of data is hosted on a separate map, which complicates planning because a robot attempting to perform a task needs to access and process information from many different maps. Also, most often correlation among the information contained in maps obtained from different sources is not evaluated or exploited. In this paper, we argue that in robotics a shift from single-source maps to a multi-layer mapping formalism has the potential to revolutionize the way robots interact with knowledge about their environment. This observation stems from the raise in metric-semantic mapping research, but expands to include in its formulation also layers containing other information sources, e.g., people flow, room semantic, or environment topology. Such multi-layer maps, here named hypermaps, not only can ease processing spatial data information but they can bring added benefits arising from the interaction between maps. We imagine that a new research direction grounded in such multi-layer mapping formalism for robots can use artificial intelligence to process the information it stores to present to the robot task-specific information simplifying planning and bringing us one step closer to high-level reasoning in robots.

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关于更智能的多层地图的潜力

机器人处理环境信息的最常见方式是使用地图。目前，每种数据都托管在单独的地图上，这使计划变得复杂，因为尝试执行任务的机器人需要访问和处理来自许多不同地图的信息。此外，通常不会评估或利用从不同来源获得的地图中包含的信息之间的相关性。在本文中，我们认为在机器人技术中，从单源地图到多层地图形式主义的转变有可能彻底改变机器人与其环境知识交互的方式。这一观察源于度量语义映射研究的兴起，但扩展到其公式中还包括包含其他信息源的层，例如人流、房间语义或环境拓扑。这种多层地图，这里称为超地图，不仅可以简化空间数据信息的处理，而且可以带来地图之间交互带来的额外好处。我们设想，一个基于机器人多层映射形式的新研究方向可以使用人工智能来处理它存储的信息，以呈现给机器人特定任务的信息，从而简化规划并使我们更接近高级推理机器人。