GNSS约束的长航带无人机影像自检校方法

黄伟; 姜三; 刘先铮; 江万寿

doi:10.13203/j.whugis20210436

GNSS约束的长航带无人机影像自检校方法

doi: 10.13203/j.whugis20210436

1. 武汉大学测绘遥感信息工程国家重点实验室，湖北武汉， 430072;
2.中国地质大学（武汉）计算机学院，湖北武汉， 430074;
3. 重庆欣荣土地房屋勘测技术研究所有限责任公司，重庆， 401120;
4. 武汉大学地球空间信息科学协同创新中心，湖北武汉， 430072

基金项目:

国家自然科学基金（42001413）。

详细信息

作者简介:
黄伟,博士,研究方向为无人机影像三维重建。hw1006@whu.edu.cn

GNSS Constrained Self-Calibration for Long Corridor UAV Image

1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430072, China;
2. School of Computer Science, China University of Geosciences, Wuhan 430074, China;
3. Chongqing Xinrong Land Housing Survey Technology Institute Co. Ltd., Chongqing 401120, China;
4. Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, 430072, China

摘要: 相机自检校直接决定无人机影像空三的精度。沿输电线路走廊采集的长航带结构无人机影像是一种典型的退化配置，对其自检校容易出现“碗状”效应。为解决该问题，传统方法往往依赖较多控制点，而本文提出的自检校方法仅需一个控制点。首先研究经典物理模型和最新的数学模型；然后在增量式SfM框架下，设计了一种联合无人机影像相机检校参数初始化和高精度差分GNSS位置信息辅助的相机自检校方法。利用两个实验区域不同采集模式下的四组无人机电力走廊影像进行无控制约束以及单个控制点约束的相机自检校实验。结果表明，本文提出的相机自检校策略在无控制点约束时，可以有效缓解长航带结构空三的“碗状”效应，减轻模型的弯曲程度，提高自检校空三的绝对精度；单个控制点约束自检校时，水平和高程精度均优于0.06 m。与当前主流开源和商业软件对比，本文算法能够得到相当或更高精度。
- 相机自检校 /
- 无人机影像 /
- 数学模型 /
- 增量式SfM /
- 差分GNSS
Abstract: Objectives Camera self-calibration determines the precision of UAV (Unmanned Aerial Vehicle) image AT (Aerial Triangulation). The UAV images collected from long transmission line corridors are critical configurations, which may lead to the "bowl effect" with camera self-calibration. To solve such problems, traditional methods rely on more than three GCPs (Ground Control Points), while this study designs a new self-calibration method with only one GCP. Methods First, two categories camera distortion models, i.e., physical and mathematical model, are studies in details. Second, within an incremental SfM (Structure from Motion) framework, a camera self-calibration method is designed, which combines the strategies for initializing camera distortion parameters and fusing high-precision GNSS (Global Navigation Satellite System) observations. Results The proposed algorithm is verified by using four UA datasets collected from two sites based on two data acquisition modes. The experimental results show that the proposed method can dramatically alleviate the "bowl effect" and improve the accuracy of AT, and the horizontal and vertical accuracy reach 0.06 m, respectively, when using one GCP. Conclusions Compared with open-source and commercial software, the proposed method achieves competitive or better performance.
- camera self-calibration /
- UAV image /
- mathematical model /
- incremental SfM /
- DGNSS

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GNSS约束的长航带无人机影像自检校方法

doi: 10.13203/j.whugis20210436

作者简介:
黄伟,博士,研究方向为无人机影像三维重建。hw1006@whu.edu.cn

GNSS Constrained Self-Calibration for Long Corridor UAV Image

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doi: 10.13203/j.whugis20210436

作者简介:
黄伟,博士,研究方向为无人机影像三维重建。hw1006@whu.edu.cn

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GNSS Constrained Self-Calibration for Long Corridor UAV Image

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GNSS约束的长航带无人机影像自检校方法

doi: 10.13203/j.whugis20210436

作者简介: 黄伟,博士,研究方向为无人机影像三维重建。hw1006@whu.edu.cn

GNSS Constrained Self-Calibration for Long Corridor UAV Image

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doi: 10.13203/j.whugis20210436

作者简介: 黄伟,博士,研究方向为无人机影像三维重建。hw1006@whu.edu.cn

English Abstract

GNSS Constrained Self-Calibration for Long Corridor UAV Image

全文HTML

目录

作者简介:
黄伟,博士,研究方向为无人机影像三维重建。hw1006@whu.edu.cn

作者简介:
黄伟,博士,研究方向为无人机影像三维重建。hw1006@whu.edu.cn