Surface Edge Explorer (see): Planning Next Best Views Directly from 3D Observations
Surface Edge Explorer (see): Planning Next Best Views Directly from 3D Observations
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Surface Edge Explorer（请参阅）：直接根据 3D 观察规划下一个最佳视图
DOI：
10.1109/icra.2018.8461098
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
P. Newman
中科院分区：
文献类型：
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作者：
Rowan Border;J. Gammell;P. Newman
测量3D场景是机器人学中的一项常见任务。系统可以通过迭代获取测量结果来自主地完成这项工作。这种规划观测以改进场景模型的过程称为下一个最佳视图(NBV)规划。NBV规划方法通常使用体(例如，体素网格)或表面(例如，三角网格)表示。体积方法很好地概括了场景之间的情况，因为它们不依赖于曲面几何体，但也不会缩放到大型场景的高分辨率模型。表面表示可以获得任何比例尺的高分辨率模型，但通常需要调整非直观的参数或多个调查阶段。提出了一种基于密度表示的无场景模型NBV规划方法。曲面边浏览器(请参见)使用当前测量的密度来检测和探索观察到的曲面边界。实验表明，在移动等距离的情况下，这种方法在更短的计算时间内提供了更好的表面复盖率，而不是现有的体积方法。
Surveying 3D scenes is a common task in robotics. Systems can do so autonomously by iteratively obtaining measurements. This process of planning observations to improve the model of a scene is called Next Best View (NBV) planning. NBV planning approaches often use either volumetric (e.g., voxel grids) or surface (e.g., triangulated meshes) representations. Volumetric approaches generalise well between scenes as they do not depend on surface geometry but do not scale to high-resolution models of large scenes. Surface representations can obtain high-resolution models at any scale but often require tuning of unintuitive parameters or multiple survey stages. This paper presents a scene-model-free NBV planning approach with a density representation. The Surface Edge Explorer (SEE) uses the density of current measurements to detect and explore observed surface boundaries. This approach is shown experimentally to provide better surface coverage in lower computation time than the evaluated state-of-the-art volumetric approaches while moving equivalent distances.