A rapid mapping method for UAV aerial photography in fire rescue

BU Xiangpeng; ZHAO Tuhao; ZHAI Haozhou; HU Tianjiang

doi:10.11714/acta.snus.ZR20250069

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A rapid mapping method for UAV aerial photography in fire rescue

Column of unmanned system (Contributing editor: WANG Hairong) | 更新时间：2026-01-19

- A rapid mapping method for UAV aerial photography in fire rescue
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 65, Issue 1, Pages: 76-84(2026)
- 作者机构：
  
  1.中山大学航空航天学院，广东深圳 518107
  2.中山大学人工智能学院，广东珠海 519080
- 作者简介：
- 基金信息：
- DOI：10.11714/acta.snus.ZR20250069
  CLC： TP242
- Received：12 April 2025，
  
  Accepted：26 April 2025，
  
  Online First：02 January 2026，
  
  Published：25 January 2026
- 稿件说明：
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卜祥鹏,赵涂昊,翟浩州等.面向消防救援指挥的无人机航拍快速成图方法[J].中山大学学报(自然科学版)(中英文),2026,65(01):76-84.

BU Xiangpeng,ZHAO Tuhao,ZHAI Haozhou,et al.A rapid mapping method for UAV aerial photography in fire rescue[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2026,65(01):76-84.
卜祥鹏,赵涂昊,翟浩州等.面向消防救援指挥的无人机航拍快速成图方法[J].中山大学学报(自然科学版)(中英文),2026,65(01):76-84. DOI： 10.11714/acta.snus.ZR20250069.

BU Xiangpeng,ZHAO Tuhao,ZHAI Haozhou,et al.A rapid mapping method for UAV aerial photography in fire rescue[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2026,65(01):76-84. DOI： 10.11714/acta.snus.ZR20250069.

摘要

提出一种基于语义信息的航拍图像拼接方法。第1阶段，基于三维向量旋转的投影变换粗配准方法，通过地理坐标转换、视锥体建模、平面交会计算以及投影矩阵构建，实现了航拍图像到统一高程平面的高效粗配准。第2阶段，基于语义分割与LightGlue特征匹配的行间误差优化方法，将粗配准后的每行图像作为独立优化单元，通过语义分割获取道路拓扑结构，结合轻量化特征匹配网络与几何约束优化，实现高精度行间配准。实验结果表明，通过构建双阶段优化模型有效克服了相机内参的依赖，最终以可视化的方式在地理信息系统界面上展示准确的受灾地点，全面、系统记录了灾情特征。

Abstract

This study proposes a semantic information-based aerial image stitching methodology. In the first stage， a coarse registration method using projection transformation based on 3D vector rotation is adopted. Through geographic coordinate conversion， view frustum modeling， plane intersection calculation， and projection matrix estimation， efficient coarse registration of aerial images onto a unified elevation plane is achieved. In the second stage， a semantic segmentation and LightGlue feature based optimization method is utilized. Each row of images after coarse registration is treated as an independent optimization unit： road topological structures are obtained via semantic segmentation， and high-precision registration is realized by combining a lightweight feature matching network with geometric constraint optimization. Experimental results show that the proposed two-stage optimization model effectively overcomes the dependence of camera intrinsics. Finally， the accurate disaster-stricken locations are visualized on the geographic system interface， enabling comprehensive and systematic recording of disaster characteristics.

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references

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Unmanned Aerial Vehicle Research Department, Nanjing University ofAeronautics and Astronautics

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