Bibliographic record
Abstract
This thesis proposes an adaptive visual-inertial loosely-coupled sensor fusion method that uses an Error State Kalman Filter (ESKF) and Fuzzy Logic Controller (FLC).The method applies to GPS denied zones.In previous attempts, researchers either tried to tune the Kalman Filter in the most precise way possible, i.e., using the Genetic Algorithm (GA) to tune the Kalman Filter, or make an adaptive Kalman Filter to prevent the divergence problem from happening.This work aims to tune the Kalman Filter and makes it adaptive to overcome the disadvantages of previous methods and minimize the error of the estimated trajectories obtained by the Kalman Filter.The fuzzy system is trained via the Particle Swarm Optimization (PSO) algorithm to achieve this goal.A comparison is held between our tuned Kalman Filter, our adaptive system, and other methods previously used by other researchers.The results show that the proposed adaptive Kalman Filter improves the accuracy and outperforms other methods of tuning Kalman Filters.In addition, our proposed approach outperforms the conventional Extended Kalman Filter (EKF) methods.Our proposed structure can deal with differences between Inertial Measurement Unit (IMU) and camera data frequencies, which are assumed to be the same in most studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".