This paper investigates the problem of planning a minimum-length tour for a three-dimensional Dubins airplane model to visually inspect a series of targets located on the ground or exterior surface of objects in an urban environment. Objects are 2.5D extruded polygons representing buildings or other structures. A visibility volume defines the set of admissible (occlusion-free) viewing locations for each target that satisfy feasible airspace and imaging constraints. The Dubins traveling salesperson problem with neighborhoods (DTSPN) is extended to three dimensions with visibility volumes that are approximated by triangular meshes. Four sampling algorithms are proposed for sampling vehicle configurations within each visibility volume to define vertices of the underlying DTSPN. Additionally, a heuristic approach is proposed to improve computation time by approximating edge costs of the 3D Dubins airplane with a lower bound that is used to solve for a sequence of viewing locations. The viewing locations are then assigned pitch and heading angles based on their relative geometry. The proposed sampling methods and heuristics are compared through a Monte-Carlo experiment that simulates view planning tours over a realistic urban environment.

翻译：本文探讨规划三维Dubins飞机模型的最低限度巡演问题,以对位于地面或城市环境中物体外部表面的一系列目标进行视觉检查。物体是代表建筑物或其他结构的2.5D挤压多边形。可见度卷界定了每个目标的可允许(无封闭)观看地点,以满足可行的空气空间和成像限制。Dubins与邻居的销售人员旅行问题(DTSPN)扩大到三个维度,其可见度以三角间距为近似值。建议采用四种抽样算法,对每卷可见度内的车辆配置进行取样,以界定DTSPN的脊椎。此外,还提议采用超常法方法,通过对3D Dubins飞机的边缘成本进行近似平衡来改善计算时间,其边框用于解决一系列观察地点。然后根据相对的几何测量结果,对观察地点进行定出位置和方向角角。拟议的取样方法和超光谱法通过蒙特-Carlo实验,对现实城市环境进行模拟规划。