超地图映射框架及其在自主语义探索中的应用

Tobias Zaenker, Francesco Verdoja, V. Kyrki
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引用次数: 7

摘要

现代智能和自主机器人应用通常要求机器人拥有比传统占用网格地图提供的更多关于其环境的信息。例如,一个执行自主语义探索任务的机器人必须在自主导航时标记它所穿越的环境中的物体。为了完成这个任务,机器人至少需要维护一个用于导航的环境占用地图,一个用于跟踪已经访问过的区域的探索地图,以及一个用于记录环境中物体位置和标签的语义地图。随着所需的地图数量的增长,应用程序必须了解和处理不同的地图表示,这可能是一个负担。我们提出了Hypermap框架,它可以管理多个不同类型的地图。在这项工作中,我们探索了框架处理占用网格层和语义多边形层的能力,但框架可以在未来扩展为新的层类型。此外,我们提出了一种从RGB-D图像中自动生成语义层的算法。我们使用语义映射的自主探索示例来演示该框架的实用性。
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Hypermap Mapping Framework and its Application to Autonomous Semantic Exploration
Modern intelligent and autonomous robotic applications often require robots to have more information about their environment than that provided by traditional occupancy grid maps. For example, a robot tasked to perform autonomous semantic exploration has to label objects in the environment it is traversing while autonomously navigating. To solve this task the robot needs to at least maintain an occupancy map of the environment for navigation, an exploration map keeping track of which areas have already been visited, and a semantic map where locations and labels of objects in the environment are recorded. As the number of maps required grows, an application has to know and handle different map representations, which can be a burden.We present the Hypermap framework, which can manage multiple maps of different types. In this work, we explore the capabilities of the framework to handle occupancy grid layers and semantic polygonal layers, but the framework can be extended with new layer types in the future. Additionally, we present an algorithm to automatically generate semantic layers from RGB-D images. We demonstrate the utility of the framework using the example of autonomous exploration for semantic mapping.
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