Quaternion-Based Unscented Kalman Filter for 6-DoF Vision-Based Inertial Navigation in GPS-Denied Regions
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".