IMU-Aided Geographic Pose Estimation Method for UAVs Using Satellite Imageries Matching

Estimating the geographic position of Unmanned Aerial Vehicles (UAVs) in the absence of Global Navigation Satellite Systems (GNSS) is crucial for enhancing flight safety. This paper presents a vision-based geolocalization method that matches images captured by onboard cameras with satellite imageries, utilizing attitude information from Inertial Measurement Units (IMUs). We introduce a two-point solution for the Perspective-n-Point (PnP) problem, specifically when the camera's pitch and roll angles are known. This approach is shown to be highly robust against image alignment errors and significantly improves position estimation accuracy. Experiments with both synthetic and real flight data confirm the effectiveness and reliability of the proposed method in practical applications.

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Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2025-01-29
DOI
https://doi.org/10.1109/lra.2025.3536285
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
0.00

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article

IMU-Aided Geographic Pose Estimation Method for UAVs Using Satellite Imageries Matching

Yongfei Li
IEEE Robotics and Automation Letters
Robotics and Sensor-Based Localization
article

IMU-Aided Geographic Pose Estimation Method for UAVs Using Satellite Imageries Matching

Yongfei Li
article en

Abstract

Estimating the geographic position of Unmanned Aerial Vehicles (UAVs) in the absence of Global Navigation Satellite Systems (GNSS) is crucial for enhancing flight safety. This paper presents a vision-based geolocalization method that matches images captured by onboard cameras with satellite imageries, utilizing attitude information from Inertial Measurement Units (IMUs). We introduce a two-point solution for the Perspective-n-Point (PnP) problem, specifically when the camera's pitch and roll angles are known. This approach is shown to be highly robust against image alignment errors and significantly improves position estimation accuracy. Experiments with both synthetic and real flight data confirm the effectiveness and reliability of the proposed method in practical applications.

IEEE Robotics and Automation LettersVol. 10(3)
Xi'an High Tech University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 98%
Robotics and Sensor-Based Localization
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