Paper Reading AI Learner

Scene Action Maps: Behavioural Maps for Navigation without Metric Information

2024-05-13 17:25:07
Joel Loo, David Hsu


Humans are remarkable in their ability to navigate without metric information. We can read abstract 2D maps, such as floor-plans or hand-drawn sketches, and use them to navigate in unseen rich 3D environments, without requiring prior traversals to map out these scenes in detail. We posit that this is enabled by the ability to represent the environment abstractly as interconnected navigational behaviours, e.g., "follow the corridor" or "turn right", while avoiding detailed, accurate spatial information at the metric level. We introduce the Scene Action Map (SAM), a behavioural topological graph, and propose a learnable map-reading method, which parses a variety of 2D maps into SAMs. Map-reading extracts salient information about navigational behaviours from the overlooked wealth of pre-existing, abstract and inaccurate maps, ranging from floor-plans to sketches. We evaluate the performance of SAMs for navigation, by building and deploying a behavioural navigation stack on a quadrupedal robot. Videos and more information is available at: this https URL.

Abstract (translated)

人类在无指标信息的情况下进行导航的能力是令人惊叹的。我们可以阅读抽象的2D地图,如平面图或手绘草图,并利用它们在未见过的丰富3D环境中进行导航,而无需先前的遍历来详细绘制这些场景。我们认为,这是通过将环境抽象地表示为相互连接的导航行为而实现的,例如"跟随走廊"或"右转",而避免在指标级别上详细、准确的空间信息。我们引入了场景动作图(SAM),一个行为拓扑图,并提出了一种可学习地图阅读方法,可以将各种2D地图解析为SAM。地图阅读从忽视的丰富预先存在的抽象和不准确地图中提取有关导航行为的突出信息,从平面图到草图。我们通过在四足机器人上构建和部署行为导航堆栈来评估SAM的导航性能。有关更多信息,请参阅:https://this URL。



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