Optimization of Terrestrial Laserscanning (TLS) Networks based on Sensitivity Analysis

工程大地测量中的灵敏度分析通常涉及基于假设或预期变形模型（刚体运动和变形）检测小变形的能力。在这个项目中，变形将通过地面激光扫描（TLS）从不同的扫描站点（视点）进行监测。目前的挑战是开发一个多标准优化方法的一阶和二阶的TLS网络的大地测量设计。这包括视点的数量和位置以及扫描设置（例如扫描旋转速度、角分辨率）。优化应涵盖完整性和敏感性。所有这些信息都可以在视点网络的规划阶段提供。开发的优化软件应适用于任何给定的物体几何形状和任何预期的变形。确保完整性意味着物体的所有表面、边缘和角落在配准的关节点云中都是可见的，并且保证了用于配准的各个扫描之间的充分重叠。使用贪婪算法，混合线性规划或元启发式方法的完整性优化提供了最小数量的TLS视点及其位置。这表示关于灵敏度的优化的初始解。最终优化的目标函数是变形模型方向上协方差矩阵的行列式。因此，从每个扫描部位获取的物点的不确定性起着实质性的作用。在这里，扫描参数，如扫描旋转速率和角分辨率，物体扫描仪的距离和入射角，被认为是。这些参数影响相对于变形模型的灵敏度。将使用元启发式方法（例如遗传算法、模拟退火）进行灵敏度优化。在优化过程中，视点的数量和它们可能位置的空间密度是变化的，直到达到全局最小值。由于两个优化目标可能导致冲突，必须提供许多可能的（帕累托最优）解决方案。该目标冲突可以在附加目标函数的情况下（多维情况）进一步扩展;例如，通过用于扫描网络的最小时间，从而确定可能的最优解的帕累托前沿。此外，还创建雷达图以支持决策。与迄今为止的研究相比，该项目同时针对最小可检测的3D变形和3D完整性进行优化。因此，一个多目标优化将被调查和观点规划工具将被开发。

Sensitivity analysis as understood in engineering geodesy usually deals with the ability to detect small deformations based on an assumed or expected deformation model (rigid body movements and distortions). In this project, deformations will be monitored by Terrestrial Laser Scanning (TLS) from different scanning sites (viewpoints). The challenge is to develop a multi-criteria optimization method for a first and second order geodetic design of a TLS network. This covers the number and positions of the viewpoints as well as the scanning settings (e.g. scan rotation velocity, angular resolution). The optimization should cover completeness and sensitivity. All this information can be provided in the planning phase for the viewpoint network. The developed optimization software should work for any given object geometry and any expected deformation. Assuring completeness means that all surfaces, edges and corners of the object are visible in the registered joint point cloud and sufficient overlap among the individual scans for registration is guaranteed. The completeness optimization using Greedy algorithms, mixed linear programming or metaheuristic approaches deliver a minimum number of TLS viewpoints together with their positions. This represents the initial solution for the optimization with respect to sensitivity. The objective function for the final optimization is the determinant of the covariance matrix in the direction of the deformation model. Therefore, the uncertainty of the object points acquired from each scanning site plays a substantial role. Here, scanning parameters like scan rotation rate and angular resolution, object-scanner distance and incidence angle, are considered. These parameters influence the sensitivity with respect to deformation models. A metaheuristic approach (e.g. genetic algorithms, simulated annealing) will be used for the sensitivity optimization. During the optimization procedure, the number of viewpoints and the spatial density of their possible positions are varied until the global minimum is reached. Since two optimization objectives may lead to a conflict, a number of possible (Pareto-optimal) solutions have to be provided. This target conflict may be further extended in case of additional objective functions (multi--dimensional case); e.g. by minimum time for scanning the network, thus a Pareto front of possible optimal-solutions is determined. Additionally, radar plots are created for decision support. In contrast to the research up to now, this project simultaneously aims at optimization with respect to minimal detectable 3D deformations and 3D completeness. Hence, a multi-criterial optimization will be investigated and a viewpoint planning tool will be developed.