Practical visual odometry for small embedded systems
Bibliographic record
Abstract
Localization and mapping are important abilities for any robot to have if it wants to navigate intelligently in the real world. The goal of the research designed in this thesis was to develop a practical embedded visual odometer that utilized common features found in real world environments. The visual odometer is a system that measures the self motion of a mobile robot using visual feeback. The developed visual odometer was tested on a custom mobile robot in several different tests that were derived from the robotic soccer domain. This systemâs performance was compared to two other systems. These systems were a KLT feature tracker based robot and a commercial shaft encoder based robot. The results of the completed tests showed that the developed visual odometerâs performance was less than expected. It also showed that this system has good potential. As well, the test results showed the limitations of using a KLT feature tracker based robot and that the commercial shaft encoder based robot also had performance less than expected.
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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".