Bibliographic record
Abstract
Navigation is a core requirement for autonomous vehicles and robotics. The objective of this thesis is to compute the navigation solution of a ground vehicle by fusing data from Inertial Navigation System (INS), Visual Odometry (VO), and Global Positioning System (GPS) using a Dual Extended Kalman Filter (DEKF) algorithm. The research in this thesis is conducted in three phases. The first phase deals with the development of a VO navigation system. In this phase the traditional Stereo Visual Odometry (SVO) methodology is analyzed, and an improvement is proposed at the pose estimation and pose optimization stages to present the Modified Stereo Visual Odometry (ModSVO) algorithm. The second phase deals with the development of INS/VO and INS/GPS integrated systems using EKF. It is shown that while accuracy improves compared to standalone sensors, but in case of VO or GPS failure the accuracy deteriorates. The third phase presents a solution to this problem by developing the INS/VO/GPS system using a Dual Extended Kalman Filter (DEKF) scheme. It is shown that the INS/VO/GPS system outperforms INS/VO and INS/GPS systems in cases of VO failure or GPS failure.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".