论文标题

关于更智能的多层图的潜力

On the Potential of Smarter Multi-layer Maps

论文作者

Verdoja, Francesco, Kyrki, Ville

论文摘要

机器人处理环境信息的最常见方法是使用地图。目前,每种数据托管在单独的地图上,这使计划复杂化,因为试图执行任务的机器人需要从许多不同的地图中访问和处理信息。同样,未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,从单源地图转变为多层映射形式主义的转变有可能革新机器人与对环境的知识互动的方式。这一观察结果源于公制语义映射研究的加剧,但扩展到其制定中还包括包含其他信息来源的层,例如人流动,房间语义或环境拓扑。这样的多层地图,这里命名为超图,不仅可以缓解处理空间数据信息,而且还可以带来图之间的相互作用带来的增加的好处。我们认为,以机器人为基础的新研究方向以机器人为基础的形式主义可以使用人工智能来处理它存储的信息,向机器人任务特定的信息提供了简化计划,并使我们更接近机器人中的高级推理。

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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