Quaternion-based Unscented Kalman Filter for 6-DoF Vision-based Inertial Navigation in GPS-denied Regions
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929614664761344 |
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| author | Ghanizadegan, Khashayar Hashim, Hashim A. |
| author_facet | Ghanizadegan, Khashayar Hashim, Hashim A. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_02768 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quaternion-based Unscented Kalman Filter for 6-DoF Vision-based Inertial Navigation in GPS-denied Regions Ghanizadegan, Khashayar Hashim, Hashim A. Robotics Systems and Control 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. |
| title | Quaternion-based Unscented Kalman Filter for 6-DoF Vision-based Inertial Navigation in GPS-denied Regions |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2412.02768 |