Efficient representation and generation of consistent 3D and 4D maps

P6的目标是开发能够管理大量3D数据的数据结构和算法。这应该允许快速访问数据的相关部分，以便计算入住率图和可视化，同时还支持高效地插入、删除和完成数据。同时，在不同时间记录的数据应该是可识别的，并永久保存在系统中。实现这一目标的关键一方面是应用核心外方法，另一方面是使用压缩算法。因此，我们将扩展开发的压缩方法，以便通过持久表示来支持原始数据的时变版本。应保证表示的高效可升级性和多分辨率能力。此外，将研究和开发建筑物表面结构的过程描述方法以提高压缩比。正如在第一阶段的实验中已经证明的那样，当前的几何压缩方法不能处理粗糙表面，如石膏、熟料或更细尺度上的墙纸，因为这些结构构成了要压缩的数据的主要部分。我们的目标是用程序噪声模型来表示这些结构，而不是压缩它们。在找到适当的程序噪声模型的相关参数后，对数据的统计分析可以确定砖石结构中的异常情况，如裂缝或其他不规则性。此外，具有相同统计特性的表面可以从压缩的平滑的底层几何图形和程序噪声模型中再现。该分项目的第三个重点将是用于若干目的的粗略尺度上稳健和有效的对称性检测方法。首先，它有助于生成占用地图，这是避障(P2/P3)所必需的。另一方面，它促进了自主探索的动作生成(P8)。此外，它们还将进一步改善几何压缩，因为自相似性已经可以在粗略尺度上检测和使用。最后，发现的自相似性还可以用于几何数据的有意义的补充或补充，这可以在必要时避免对象的多次越界。典型的例子是在整个建筑中不止一次出现的海湾或塔楼，通常必须从几个侧面捕捉。为此，申请人之一共同开发的基于基元的点云重建方法将首先整合到P5的重建方法中，然后扩展到可以在点云中检测到的对称子部分。

The aim of P6 is the development of data structures and algorithms that can manage large amounts of 3D data. This should allow for both quick access to relevant parts of the data for the calculation of occupancy maps and the visualization and at the same time also support efficient insertion, deletion, and completion of the data. At the same time data recorded at different times should be identifiable persistently maintained in the system. The key to achieve this goal is on the one hand the application of out-of-core approaches and on the other hand the use of compression algorithms. Therefore, we will extend the developed compression methods in order to support time varying versions of the raw data by a persistent representation. The efficient upgradeability and the multi-resolution capability of the representation should be guaranteed.In addition methods for procedural description of surface structures of buildings will be investigated and exploited to improve the compression rates. As has already been demonstrated in experiments in phase I, current methods for geometry compression are not able to handle rough surfaces, such as plaster, clinker, or on finer scales also wallpapers, since these structures constitute a major part of the entropy of the data to be compressed. We aim for representing these structures by procedural noise models, rather than to compress them. Having found the relevant parameters of a suitable procedural noise model statistical analysis of the data allows for the determination of anomalies such as cracks or other irregularities in the masonry. Furthermore, a surface with the same statistical characteristic can be reproduced from the compressed smoothed underlying geometry and the procedural noise model. The third focus of the subproject will be on methods for robust and efficient symmetry detection on rough scales which serves several purposes. Firstly, it helps in the production of occupancy maps, which for obstacle avoidance (P2/P3) are required. On the other hand, it facilitates the action generation at the autonomous exploration (P8). In addition, they will also improve the geometry compression further, since self-similarity can be detected and used on coarse scales already. Finally, discovered self-similarities can also be used for meaningful supplement or completion of geometry data, which can, if necessary, avoid multiple overflights of an object. Typical examples are bays or towers that appear more than once in the overall building and usually have to be captured from several sides. To this end, the primitive-based reconstruction method for point clouds co-developed by one of the applicants will first be integrated into the reconstruction methods of P5 and second be extended to symmetric subparts that can be detected in the point cloud.