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基于点云的结构检测覆盖路径规划优化框架。

A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection.

机构信息

Department of Electrical Engineering, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil.

Department of Electronics Engineering, Federal Center for Technological Education of Rio de Janeiro, Rio de Janeiro 20271-110, Brazil.

出版信息

Sensors (Basel). 2021 Jan 15;21(2):570. doi: 10.3390/s21020570.

DOI:10.3390/s21020570
PMID:33467417
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7830133/
Abstract

Different practical applications have emerged in the last few years, requiring periodic and detailed inspections to verify possible structural changes. Inspections using Unmanned Aerial Vehicles (UAVs) should minimize flight time due to battery time restrictions and identify the terrain's topographic features. In this sense, Coverage Path Planning (CPP) aims at finding the best path to coverage of a determined area respecting the operation's restrictions. Photometric information from the terrain is used to create routes or even refine paths already created. Therefore, this research's main contribution is developing a methodology that uses a metaheuristic algorithm based on point cloud data to inspect slope and dams structures. The technique was applied in a simulated and real scenario to verify its effectiveness. The results showed an increasing 3D reconstructions' quality observing optimizing photometric and mission time criteria.

摘要

在过去的几年中，出现了不同的实际应用，需要定期进行详细检查以验证可能的结构变化。使用无人机 (UAV) 进行的检查应尽量减少飞行时间，因为电池时间有限，并确定地形的地形特征。在这种意义上，覆盖路径规划 (CPP) 旨在找到最佳路径以覆盖特定区域，同时尊重操作限制。利用地形的光度信息来创建路线，甚至改进已经创建的路径。因此，本研究的主要贡献是开发一种使用基于点云数据的元启发式算法来检查边坡和水坝结构的方法。该技术已在模拟和真实场景中应用，以验证其有效性。结果表明，通过优化光度和任务时间标准，观察到 3D 重建质量的提高。

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