The hierarchical atlas
The hierarchical atlas
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层次图集
DOI：
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发表时间：
2005
影响因子：
7.8
通讯作者：
H. Choset
中科院分区：
文献类型：
--
作者：
Brad Lisien;D. Morales;David Silver;G. Kantor;Ioannis M. Rekleitis;H. Choset
本文提出了一种新的地图，专门设计的机器人在大环境中运行，并可能在更高的维度。我们称这个地图为层次地图集，因为它是一个多层次和多分辨率的表示。对于本文来说，层次地图集有两个层次：在最高层次有一个拓扑图，它将自由空间组织成较低层次的子图。较低级别的子映射只是特征的集合。分层地图集允许我们在局部区域执行计算和运行估计技术，如卡尔曼滤波，而不必关联和关联整个地图的数据。这提供了一种手段，探索和映射大的环境中存在的不确定性的过程中命名的分层同时定位和映射。除了组织自由空间的信息外，该地图还引入了定义良好的基于传感器的控制律和可证明的完整策略来探索未知区域。生成的地图也可用于其他任务，如导航、避障和全局定位。实验结果显示成功的地图建设和随后使用的地图在大规模的空间。
This paper presents a new map specifically designed for robots operating in large environments and possibly in higher dimensions. We call this map the hierarchical atlas because it is a multilevel and multiresolution representation. For this paper, the hierarchical atlas has two levels: at the highest level there is a topological map that organizes the free space into submaps at the lower level. The lower-level submaps are simply a collection of features. The hierarchical atlas allows us to perform calculations and run estimation techniques, such as Kalman filtering, in local areas without having to correlate and associate data for the entire map. This provides a means to explore and map large environments in the presence of uncertainty with a process named hierarchical simultaneous localization and mapping. As well as organizing information of the free space, the map also induces well-defined sensor-based control laws and a provably complete policy to explore unknown regions. The resulting map is also useful for other tasks such as navigation, obstacle avoidance, and global localization. Experimental results are presented showing successful map building and subsequent use of the map in large-scale spaces.