The aging and increasing complexity of infrastructures make efficient inspection planning more critical in ensuring safety. Thanks to sampling-based motion planning, many inspection planners are fast. However, they often require huge memory. This is particularly true when the structure under inspection is large and complex, consisting of many struts and pillars of various geometry and sizes. Such structures can be represented efficiently using implicit models, such as neural Signed Distance Functions (SDFs). However, most primitive computations used in sampling-based inspection planner have been designed to work efficiently with explicit environment models, which in turn requires the planner to use explicit environment models or performs frequent transformations between implicit and explicit environment models during planning. This paper proposes a set of primitive computations, called Inspection Planning Primitives with Implicit Models (IPIM), that enable sampling-based inspection planners to entirely use neural SDFs representation during planning. Evaluation on three scenarios, including inspection of a complex real-world structure with over 92M triangular mesh faces, indicates that even a rudimentary sampling-based planner with IPIM can generate inspection trajectories of similar quality to those generated by the state-of-the-art planner, while using up to 70x less memory than the state-of-the-art inspection planner.

翻译：基础设施的老化与日益复杂使得高效检测规划对于保障安全愈发关键。得益于基于采样的运动规划方法，许多检测规划器运行迅速。然而，它们通常需要巨大的内存开销。当被检测结构规模庞大且结构复杂，包含大量几何形状与尺寸各异的支柱和立柱时，这一问题尤为突出。此类结构可采用隐式模型（如神经符号距离函数）高效表示。然而，基于采样的检测规划器中使用的大多数基元计算均设计为与显式环境模型高效协同工作，这反过来要求规划器使用显式环境模型，或在规划过程中频繁进行隐式与显式环境模型之间的转换。本文提出了一组基元计算，称为基于隐式模型的检测规划基元，使得基于采样的检测规划器能够在规划过程中完全使用神经符号距离函数表示。在三种场景下的评估（包括对包含超过9200万个三角网格面的复杂真实世界结构的检测）表明，即使采用IPIM的简易基于采样规划器，也能生成与最先进规划器质量相当的检测轨迹，同时内存使用量比最先进的检测规划器减少高达70倍。