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首页> 外文会议>2011 IEEE International Conference on Systems, Man, and Cybernetics >Hierarchical localization using compact hybrid mapping for large-scale unstructured environments
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Hierarchical localization using compact hybrid mapping for large-scale unstructured environments

机译：使用紧凑型混合映射的分层本地化，用于大规模非结构化环境

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摘要

Hierarchical localization frameworks provide efficient means for localizing a robot topologically and geometrically. The metric localization performance is enhanced since the searchable space is confined to a previously identified topological place. This adapts well to large-scale environments. In this paper, a two-level hierarchical localization using a compact hybrid map is presented. The map preserves information set at two different abstractions and resolutions, and possesses both geometric and non-geometric properties. The coarse-resolution information enables global topological localization through place matching. The higher-resolution enables local metric localization through triangulation. The presented hierarchical localization is totally independent on the robot's motion model. For effectiveness in both mapping and localization, the map is constructed based on information-theoretic evaluation that selects only highly qualitative information. The approach is demonstrated using a vision sensor and the scale invariant feature transform.
机译：分层本地化框架提供了拓扑和几何上的机器人的有效手段。由于可搜索的空间限制在先前识别的拓扑地位，因此度量定位性能得到增强。这适用于大规模环境。本文介绍了使用紧凑型混合图的两级分层本地化。地图保留在两个不同的抽象和分辨率下设置的信息，并拥有几何和非几何属性。粗辨率信息通过地方匹配使全球拓扑定位能够实现。更高分辨率使通过三角测量实现本地度量定位。所呈现的分层本地化在机器人的运动模型上完全独立。为了在映射和定位方面的有效性，地图是基于信息理论评估构建的，用于仅选择高度定性信息。使用视觉传感器和秤不变特征变换来说明该方法。

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