通过整合低成本无人驾驶飞行器图像和移动地图系统数据来生成无的三维地图
Mohammad Gholami Farkoushi1, Seunghwan Hong2, Hong-Gyoo Sohn1
1School of Civil and Environmental Engineering, Yonsei University, Seodaemun-gu, Seoul 03722, Republic of Korea.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究提出了一个新的框架,集成移动地图系统 (MMS) 和无人机 (UAV) 数据,用于高准确度的3D城市地图. 该方法通过结合地面和空中数据来提高准确性和细节性,有利于城市规划和基础设施监测.
科学领域:
- 地理学工程 工程地质学
- 计算机视觉 计算机视觉
- 城市信息学 城市信息学
背景情况:
- 传统的3D城市绘图面临着来自单一来源的数据封闭和有限细节的挑战.
- 整合地面和空中数据为全面的城市建模提供了互补的优势.
研究的目的:
- 通过融合移动地图系统 (MMS) 和无人机 (UAV) 数据,开发一个创新的框架来生成无,高准确度的3D城市地图.
- 克服单一源地图的局限性,例如空中视图遮蔽和地面水平垂直细节缺陷.
主要方法:
- 使用布模拟过来从MMS数据中提取地面点.
- 使用深度学习 (U2-Net) 来从无人机图像中提取特征.
- 应用逆视角映射和LightGlue用于交叉视图数据对齐和集成到结构从运动管道中.
主要成果:
- 通过融合数据,在3D城市模型中实现了显著的准确性改进.
- 在验证数据集中证明了0.131m的根平均平方误差.
- 成功生成了无,高准确度的3D城市地图.
结论:
- 拟议的框架为增强的3D城市绘图提供了灵活和可扩展的解决方案.
- 这种方法改善了地理空间分析,基础设施监测和城市规划能力.
- 与单一来源方法相比,MMS和UAV数据的融合提供了更好的结果.
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