Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This thesis addresses the problem of automatically constructing a visual representation of an unknown environment that is useful for robotic navigation, localization and exploration. There are two main contributions. First, the concept of the visual map is developed, a representation of the visual structure of the environment, and a framework for learning this structure is provided. Second, methods for automatically constructing a visual map are presented for the case when limited information is available about the position of the camera during data collection. The core concept of this thesis is that of the visual map, which models a set of image-domain features extracted from a scene. These are initially selected using a measure of visual saliency, and subsequently modelled and evaluated for their utility for robot pose estimation. Experiments are conducted demonstrating the feature learning process and the inferred models' reliability for pose inference. The second part of this thesis addresses the problem of automatically collecting training images and constructing a visual map. First, it is shown that visual maps are self-organizing in nature, and the transformation between the image and pose domains is established with minimal prior pose information. Second, it is shown that visual maps can be constructed reliably in the face of uncertainty by selecting an appropriate exploration strategy. A variety of such strategies are presented and these approaches are validated experimentally in both simulated and real-world settings.
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.
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 it