基于感知的室内建筑环境下无人机鲁棒定位标签放置规划

IF 4.7 2区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computing in Civil Engineering Pub Date : 2022-10-27 DOI:10.1061/JCCEE5/CPENG-5068

Navid Kayhani, Angela P. Schoellig, B. McCabe

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引用次数: 2

摘要

基于标签的视觉惯性定位是实现低成本无人机在室内建筑环境中自主数据采集任务的一种轻量级方法。然而，在动态施工现场找到最佳的标签配置(即数量、大小和位置)仍然具有挑战性。本文提出了一种基于感知遗传算法的标签放置规划器(PGA-TaPP)，结合项目进度、安全要求和无人机的可定位性，利用4D-BIM确定最优标签配置。该方法通过最大化用户指定的兴趣区域(roi)的可定位性，同时限制安装成本，为标签放置提供了4D计划。利用Fisher信息矩阵(FIM)量化定位能力，并将其封装在可导航网格中。实验结果表明，该方法能够有效地找到最优的4D标签放置方案，用于无人机在施工室内场地的鲁棒定位。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Perception-aware Tag Placement Planning for Robust Localization of UAVs in Indoor Construction Environments

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.

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来源期刊

Journal of Computing in Civil Engineering 工程技术-工程：土木

CiteScore

11.90

自引率

7.20%

发文量

审稿时长

6 months

期刊介绍： The Journal of Computing in Civil Engineering serves as a resource to researchers, practitioners, and students on advances and innovative ideas in computing as applicable to the engineering profession. Many such ideas emerge from recent developments in computer science, information science, computer engineering, knowledge engineering, and other technical fields. Some examples are innovations in artificial intelligence, parallel processing, distributed computing, graphics and imaging, and information technology. The journal publishes research, implementation, and applications in cross-disciplinary areas including software, such as new programming languages, database-management systems, computer-aided design systems, and expert systems; hardware for robotics, bar coding, remote sensing, data mining, and knowledge acquisition; and strategic issues such as the management of computing resources, implementation strategies, and organizational impacts.