Surface Edge Explorer（SEE）：直接从三维观测规划下一个最佳视图
Surface Edge Explorer (SEE): Planning Next Best Views Directly from 3D Observations
摘要：
测量3D场景是机器人技术中的一项常见任务。系统可以通过迭代地获得测量值来自主地这样做。这种规划观测以改进场景模型的过程称为次佳视图（NBV）规划。NBV规划方法通常使用体积（例如，体素网格）或表面（例如，三角网格）表示。体积方法可以很好地在场景之间推广，因为它们不依赖于表面几何形状，但不能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的高分辨率模型，但通常需要调整不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的NBV规划方法。Surface Edge Explorer（SEE）使用电流测量的密度来检测和探索观察到的表面边界。实验表明，这种方法提供更好的表面覆盖率在较低的计算时间比评估国家的最先进的体积的方法，同时移动相等的距离。
英文摘要：
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.
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