Semantic 3D Octree Maps based on Conditional Random Fields
Semantic 3D Octree Maps based on Conditional Random Fields
复制标题
基于条件随机场的语义 3D 八叉树图
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
D. Paulus
中科院分区：
文献类型：
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作者：
D. Lang;S. Friedmann;D. Paulus
本文提出了一个基于MapMap映射框架的多标号和分辨率八叉树的三维语义室外映射系统。点云的语义标记使用条件随机场。为了加快条件随机场的速度，我们使用一种基于体素网格的自适应图下采样方法和有向残差直方图算子来描述局部点云分布。我们验证了所提出的分类和地图表示方法对现实世界的三维点云数据。所提出的分类方法达到了约96%的整体精度。将分类结果集成到地图数据结构中提供了解决复杂任务设置的机会。此外，所提出的方法的运行时允许的分类到一个实时的3D语义室外映射系统的集成。
In this paper we present a 3D semantic outdoor mapping system with multi-label and resolution octree maps based on the OctoMap mapping framework. The semantic labeling of point clouds uses conditional random fields. Speeding up the conditional random field, we use an adaptive graph downsampling method based on voxel grids and the histogram-of-oriented-residuals operator to describe the local point cloud distribution. We validate the proposed classification and map representation approach on real-world 3D point cloud data. The presented classification approach achieves an overall precision about 96 %. The integration of the classification results into the map data structure offers the opportunity to solve complex task settings. Furthermore, the runtime of the presented approach allows an integration of the classification into a real-time 3D semantic outdoor mapping system.