Bibliographic record
Abstract
The two main challenges offered by Simultaneous Localization and Mapping (SLAM) are that of observability and extending state estimation to exploration.This thesis explores and uses solutions to render the SLAM problem observable, by proposing the Reconfigurable Extended Kalman Filter (EKF) that addresses imposing observability, maintaining observability and choice of observability constraints.Additionally, Bayesian theory and Dempster-Shafer theory of evidential reasoning are analyzed, and Occupancy grid based maps based on Dempster-Shafer theory of evidential reasoning are created and analyzed in large environment for their potential use in exploration and obstacle avoidance.Tackling both issues with different algorithms yield better solutions to the challenges offered by robotic exploration, and this is demonstrated through simulation results in representative environments.To my amazing parents, who taught me the value of hard work, patience and perseverance.I am very grateful to my advisor, Dr.V. Aitken for being a kind and patient teacher.Dr.L.Tabrizi, whose teaching left me in awe of control systems, has been and will continue to be an inspiring role model.I will always cherish how Rytis.V gently pushed me to work harder, learn better, and to see things from a new perspective
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".