Generalized q-Method Relative Pose Estimation for UAVs with Onboard Sensor Measurements

The q-method for pose estimation utilizes on-board measurement vectors of reference objects to calculate air vehicle position and orientation with respect to an Inertial frame. This new method solves for the quaternion eigenvalue solution of the optimal pose to minimize the error in the derived system of equations. The generalized q-method extends Davenport's q-method for satellite attitude estimation by incorporating inertial position into the relative model and eliminating assumptions throughout the derivation that require spacecraft applications. Thus, the pose estimation model is developed and implemented for UAV applications using an onboard camera to obtain measurements in a controlled environment. Combined with numerical methods, algorithm outputs for position and orientation are validated against truth data to prove accurate estimation despite sensor error.

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Publication Details

Journal
Sensors
Published
2025-03-20
DOI
https://doi.org/10.3390/s25061939
Primary Topic
Inertial Sensor and Navigation
Type
article
Field-Weighted Citation Impact
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article

Generalized q-Method Relative Pose Estimation for UAVs with Onboard Sensor Measurements

Kyl Stanfield, Ahmad Bani Younes, Mohammad Hayajneh
Sensors
Inertial Sensor and Navigation
article

Generalized q-Method Relative Pose Estimation for UAVs with Onboard Sensor Measurements

Kyl Stanfield, Ahmad Bani Younes, Mohammad Hayajneh
article en

Abstract

The q-method for pose estimation utilizes on-board measurement vectors of reference objects to calculate air vehicle position and orientation with respect to an Inertial frame. This new method solves for the quaternion eigenvalue solution of the optimal pose to minimize the error in the derived system of equations. The generalized q-method extends Davenport's q-method for satellite attitude estimation by incorporating inertial position into the relative model and eliminating assumptions throughout the derivation that require spacecraft applications. Thus, the pose estimation model is developed and implemented for UAV applications using an onboard camera to obtain measurements in a controlled environment. Combined with numerical methods, algorithm outputs for position and orientation are validated against truth data to prove accurate estimation despite sensor error.

SensorsVol. 25(6)
Hashemite University (JO), San Diego State University (US)
Affordable and clean energy
Openalex Percentile: Top 93%
Inertial Sensor and Navigation
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