Rumination Meets VSLAM: You Don't Need to Build All the Submaps in Realtime
Bibliographic record
Abstract
In the application of visual navigation, submap-based VSLAM has become one of the most robust monocular solutions in recent years, which is able to resume tracking by multi-submap maintenance and merging. However, due to the lack of long-term data association, the global consistency of submaps cannot be guaranteed in the existing work. Considering the fact that long-term data association does not have to be produced in realtime, we propose a VSLAM system with realtime and non-realtime hybrid style, RUMI-SLAM. Inspired by the rumination of mammalians that processes food in various stomaches and absorbs it in one stomach, RUMI-SLAM performs distributed submap building and centralized submap management. Building additional submaps in parallel leads to enriched mapping elements and enhanced data association across submaps, especially in challenging situations such as interleaved blurry and texture-poor frames. The experimental results demonstrate the superiority of RUMI-SLAM over the existing VSLAM systems, especially the robustness to challenging situations. We also provide real-robot experiments to demonstrate our RUMI-SLAM in the application of visual navigation. Our study provides a novel submap-based VSLAM framework, which achieves a robust and globally consistent performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".