Abstract Exploration is a task in which autonomous mobile robots incrementally discover features of interest in initially unknown environments. We consider the problem of exploration for map building, in which a robot explores an indoor environment in order to build a metric map. Most of the current exploration strategies used to select the next best locations to visit ignore prior knowledge about the environments to explore that, in some practical cases, could be available. In this paper, we present an exploration strategy that evaluates the amount of new areas that can be perceived from a location according to a priori knowledge about the structure of the indoor environment being explored, like the floor plan or the contour of external walls. Although this knowledge can be incomplete and inaccurate (e.g., a floor plan typically does not represent furniture and objects and consequently may not fully mirror the structure of the real environment), we experimentally show, both in simulation and with real robots, that employing prior knowledge improves the exploration performance in a wide range of settings.

中文翻译：

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机器人使用不完整和不准确的先验知识探索室内环境

摘要探索是一项任务，其中自主移动机器人在最初未知的环境中逐渐发现感兴趣的特征。我们考虑地图构建的探索问题，其中机器人探索室内环境以构建度量地图。大多数当前用于选择下一个最佳位置的探索策略都忽略了有关要探索的环境的先验知识，在某些实际情况下，这些知识是可用的。在本文中，我们提出了一种探索策略，该策略根据有关正在探索的室内环境结构的先验知识（如平面图或外墙的轮廓）来评估可以从一个位置感知到的新区域的数量。尽管这些知识可能不完整和不准确（例如，