Vision-based localization, map building and obstacle reconstruction in ground plane environments
Bibliographic record
Abstract
The work described in this thesis develops the theory of 3D obstacle reconstruction and map building problems in the context of a robot, or a team of robots, equipped with one camera mounted on board. The study is composed of many problems representing the different phases of actions taken by the robot. This thesis first studies the problem of image matching for wide baseline images taken by moving robots. The ground plane is detected and the inter-image homography induced by the ground plane is calculated. A novel technique for ground plane matching is introduced using the overhead view transformation. The thesis then studies the simultaneous localization and map building (SLAM) problem for a team of robots collaborating in the same work site. A vision-based technique is introduced in this thesis to solve the SLAM problem. The third problem studied in this thesis is the 3D obstacle reconstruction of the obstacles lying on the ground surface. In this thesis a Geometric/Variational level set method is proposed to reconstruct the obstacles detected by the robots.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".