Bibliographic record
Abstract
Feature based Visual SLAM is a critical task in computer vision in which Feature extraction process which involves keypoint and descriptor generation is a crucial and time-consuming component. In collaboration with an industry partner, this research explores on-camera acceleration for feature extraction in VSLAM. Focusing on ORB SLAM 3, a prominent open-source Visual SLAM algorithm, we conduct comprehensive timing and performance analysis. Additionally, we assess the viability of replacing ORB with AKAZE for feature extraction. Experiments span Linux, ROS, and real-time environments, utilizing the groundbreaking "Bottlenose" camera, which provides images alongside FAST corners and AKAZE descriptors. Integration of Bottlenose with ORB SLAM 3 through the GigE vision interface enables real-time testing. Our study highlights substantial improvements in feature extraction and tracking times, with noteworthy reductions in CPU usage. On-camera acceleration emerges as a promising avenue for enhancing Visual SLAM, potentially facilitating higher frame rates and resolutions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".