ASSESSMENTS OF NONLINEAR LEAST SQUARES METHODS FOR UAV VISION BASED NAVIGATION
Bibliographic record
Abstract
In recent years, the UAV’s are increasingly becoming main part of both military and commercial operations, where accurate pose estimation is considered as a critical problem to be investigated. UAV pose estimation problem can be investigated through vision based navigation (VBN) approach where visual sensors are augmented with the traditional IMU unit. VBN is based on localizing set of features (with known coordinates) on the ground and find their matches in the image taken by an imaging sensor on the UAV. Then, through Collinearity equation, object space transformation parameters are estimated such that these matches are transformed into position information. Two major problems in VBN are addressed. The first one is when general tilted aerial platform is used which leads to general photograph without the near vertical assumption. The limitations in traditional least square approach for solving the collinearity equation to deal with this situation have been presented and the solution to this problem is introduced using five different non-linear least squares methods. These methods are Trust region, Trust region dogleg algorithm, Levenberg-Marquardt, Nelder-Mead simplex direct search, and Quasi-Newton line search method. The second one is number of matches necessary for solving the collinearity equation which is highly required to be minimal as possible to save the cost needed for pre surveyed landmarks before the mission. This problem is addressed using the proposed nonlinear least squares methods mentioned above. Assessment of these methods in employing the SURF 64 and SURF 128 algorithm for image matching is investigated.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".