PEBO-SLAM: Observer Design for Visual Inertial SLAM With Convergence Guarantees
Bibliographic record
Abstract
In this article, we introduce a new parameterization for the problem of visual inertial simultaneous localization and mapping (VI-SLAM), i.e., for a robot only equipped with a single monocular camera and an inertial measurement unit. In this problem, the system state evolves on the nonlinear manifold$ \mathit{SE}(3)\times \mathbb {R}^{3\mathit{n}}$, on which we design dynamic extensions such that the deterministic VI-SLAM problem can be reformulated—without any approximation—into onlineconstant parameteridentification, expressed as a linear regression. This demonstrates that deterministic VI-SLAM can be translated into a linear least squares problemgloballyandexactly. Based on this observation, we propose a novel SLAM observer, following the recently established parameter estimation-based observer methodology. A notable merit of the proposed observer is its almost global asymptotic stability. Unlike most existing methods, its convergence does not rely on persistency of excitation or uniform complete observability—assumptions commonly used in stability proofs that can be challenging to satisfy in real-world applications.
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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 | 0.002 | 0.007 |
| 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 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".