论文标题
探索室内环境，预测部分观察到的房间的布局
Exploration of Indoor Environments Predicting the Layout of Partially Observed Rooms
论文作者
论文摘要
我们考虑探索任务，其中自动移动机器人会逐步构建最初未知的室内环境的地图。在这样的任务中，机器人对接下来要移动的地方做出了一系列决策，通常是基于对环境中观察到的部分的知识。在本文中，我们提出了一种方法，该方法利用了环境未知部分的几何结构的预测，以改善勘探性能。特别是，我们利用一种现有方法，该方法从部分网格图开始重建环境的布局，并根据代表室内环境的规律性的几何特征来预测部分观察到的房间的形状。然后，我们最初采用预测的布局来估算机器人将从候选地点观察到的新区域的数量，以告知下一个最佳位置的选择，并在没有发现进一步的相关区域时尽早停止探索。实验活动表明，我们的方法能够有效预测部分观察到的房间的布局，并使用此类知识来加快探索。
We consider exploration tasks in which an autonomous mobile robot incrementally builds maps of initially unknown indoor environments. In such tasks, the robot makes a sequence of decisions on where to move next that, usually, are based on knowledge about the observed parts of the environment. In this paper, we present an approach that exploits a prediction of the geometric structure of the unknown parts of an environment to improve exploration performance. In particular, we leverage an existing method that reconstructs the layout of an environment starting from a partial grid map and that predicts the shape of partially observed rooms on the basis of geometric features representing the regularities of the indoor environment. Then, we originally employ the predicted layout to estimate the amount of new area the robot would observe from candidate locations in order to inform the selection of the next best location and to early stop the exploration when no further relevant area is expected to be discovered. Experimental activities show that our approach is able to effectively predict the layout of partially observed rooms and to use such knowledge to speed up the exploration.
