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arXiv 2501.14486cs.RO
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用于基础设施巡检的视觉-激光雷达地图对齐

Visual-Lidar Map Alignment for Infrastructure Inspections

University of Waterloo(滑铁卢大学);University of California, Los Angeles(加利福尼亚大学洛杉矶分校)

Jake McLaughlin, Nicholas Charron, Sriram Narasimhan

AI总结:

针对无GPS环境下重复巡检的3D地图难以自动对齐的问题,提出一种结合视觉与激光雷达的地图对齐算法及专用数据集,将地图对齐与SLAM解耦以提升位置识别鲁棒性,支持长期资产退化监测。

摘要:

常规和重复性的基础设施巡检在安全、效率和一致性方面面临挑战,因为它们通常在充满挑战或危险的环境中由人工执行。它们还可能将主观性和错误引入流程,导致不良结果。同时定位与建图(SLAM)提供了一个机会,可以生成高质量3D地图,用于提取准确且客观的巡检数据。然而,许多SLAM算法在无GPS环境中自动对齐重复巡检的3D地图方面能力有限。这一限制阻碍了实际的长期资产健康评估,因为需要繁琐的手动对齐来关联以往巡检的扫描数据。本文提出了一种利用视觉和激光雷达数据的通用地图对齐算法,以提高位置识别的鲁棒性,并提出了一个专为连续巡检定制的基础设施数据集。通过将地图对齐与SLAM分离,我们的方法增强了基础设施巡检流程,支持监测资产随时间的退化,并通过允许探索现有Multi-session SLAM算法之外的内容,推动了SLAM研究。

英文摘要:

Routine and repetitive infrastructure inspections present safety, efficiency, and consistency challenges as they are performed manually, often in challenging or hazardous environments. They can also introduce subjectivity and errors into the process, resulting in undesirable outcomes. Simultaneous localization and mapping (SLAM) presents an opportunity to generate high-quality 3D maps that can be used to extract accurate and objective inspection data. Yet, many SLAM algorithms are limited in their ability to align 3D maps from repeated inspections in GPS-denied settings automatically. This limitation hinders practical long-term asset health assessments by requiring tedious manual alignment for data association across scans from previous inspections. This paper introduces a versatile map alignment algorithm leveraging both visual and lidar data for improved place recognition robustness and presents an infrastructure-focused dataset tailored for consecutive inspections. By detaching map alignment from SLAM, our approach enhances infrastructure inspection pipelines, supports monitoring asset degradation over time, and invigorates SLAM research by permitting exploration beyond existing multi-session SLAM algorithms.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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