Tag-based visual-inertial localization is a lightweight method for enabling autonomous data collection missions of low-cost unmanned aerial vehicles (UAVs) in indoor construction environments. However, finding the optimal tag configuration (i.e., number, size, and location) on dynamic construction sites remains challenging. This paper proposes a perception-aware genetic algorithm-based tag placement planner (PGA-TaPP) to determine the optimal tag configuration using 4D-BIM, considering the project progress, safety requirements, and UAV's localizability. The proposed method provides a 4D plan for tag placement by maximizing the localizability in user-specified regions of interest (ROIs) while limiting the installation costs. Localizability is quantified using the Fisher information matrix (FIM) and encapsulated in navigable grids. The experimental results show the effectiveness of our method in finding an optimal 4D tag placement plan for the robust localization of UAVs on under-construction indoor sites.
翻译：在室内建筑环境中对低成本无人驾驶飞行器(无人驾驶飞行器)进行自动数据收集任务,基于标签的视觉-免疫定位是一种轻量级方法,可以让室内建筑环境中的低成本无人驾驶飞行器(无人驾驶飞行器)进行自主数据收集任务;然而,在动态建筑工地找到最佳标签配置(即数量、大小和位置)仍具有挑战性;本文件提议采用基于认知的遗传算法标签定位定位仪(PGA-TAPP)来确定4D-BIM的最佳标签配置,同时考虑到项目进度、安全要求和无人驾驶飞行器的可定位性。拟议方法提供了一个4D标签配置计划,在用户指定利益区域最大限度地实现定位性,同时限制安装成本。利用渔业信息矩阵(FIM)和在可导航网中封装的本地化量化了本地性。实验结果表明,我们的方法在寻找最佳的4D标签定位计划,以便在未开工室内场地对无人驾驶飞行器进行稳健的定位方面是有效的。
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