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arXiv · 2412.02768

Quaternion-based Unscented Kalman Filter for 6-DoF Vision-based Inertial Navigation in GPS-denied Regions

Khashayar Ghanizadegan·Hashim A. Hashim
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Abstract

This paper investigates the orientation, position, and linear velocity estimation problem of a rigid-body moving in three-dimensional (3D) space with six degrees-of-freedom (6 DoF). The highly nonlinear navigation kinematics are formulated to ensure global representation of the navigation problem. A computationally efficient Quaternion-based Navigation Unscented Kalman Filter (QNUKF) is proposed on $\mathbb{S}^{3}\times\mathbb{R}^{3}\times\mathbb{R}^{3}$ imitating the true nonlinear navigation kinematics and utilize onboard Visual-Inertial Navigation (VIN) units to achieve successful GPS-denied navigation. The proposed QNUKF is designed in discrete form to operate based on the data fusion of photographs garnered by a vision unit (stereo or monocular camera) and information collected by a low-cost inertial measurement unit (IMU). The photographs are processed to extract feature points in 3D space, while the 6-axis IMU supplies angular velocity and accelerometer measurements expressed with respect to the body-frame. Robustness and effectiveness of the proposed QNUKF have been confirmed through experiments on a real-world dataset collected by a drone navigating in 3D and consisting of stereo images and 6-axis IMU measurements. Also, the proposed approach is validated against standard state-of-the-art filtering techniques. IEEE Keywords: Localization, Navigation, Unmanned Aerial Vehicle, Sensor-fusion, Inertial Measurement Unit, Vision Unit.

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Khashayar Ghanizadegan, Hashim A. Hashim. 2024-12-03. Quaternion-based Unscented Kalman Filter for 6-DoF Vision-based Inertial Navigation in GPS-denied Regions. https://doi.org/10.1109/tim.2024.3509582

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