融合视觉与激光数据，实现无GPS环境下的自动地图对齐。
Visual-Lidar Map Alignment for Infrastructure Inspections
- 用视觉和激光数据联合提升定位鲁棒性
- 可自动对齐多次巡检的3D地图，无需人工干预
- 适合长期基础设施健康监测，推动SLAM研究
常规的基础设施巡检依赖人工，存在安全风险、效率低下和结果主观等问题，尤其在无GPS环境下难以实现多轮巡检数据的自动关联。本文提出一种新型地图对齐算法，融合视觉与激光雷达数据，增强在复杂环境中的场景识别能力，并构建了一个面向连续巡检的专用数据集。通过将地图对齐从传统SLAM中解耦，该方法显著提升巡检流程自动化水平，支持长期资产退化监测，同时为多时段SLAM研究提供新范式。
原文摘要 · Abstract (English)
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.
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