Quaternion-Based Unscented Kalman Filter for 6-DoF Vision-Based Inertial Navigation in GPS-Denied Regions
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Bibliographic record
Abstract
This article investigates the orientation, position, and linear velocity estimation problem of a rigid-body moving in 3-D 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 to imitate the true nonlinear navigation kinematics and utilize onboard visual-inertial navigation (VIN) units to achieve successful Global Positioning System (GPS)-denied navigation. The proposed QNUKF is designed in the 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 3-D space, while the six-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 3-D and consisting of stereo images and six-axis IMU measurements. Also, the proposed approach is validated against state-of-the-art filtering techniques.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it