Relative Constrained SLAM for Robot Navigation
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a relative-constrained SLAM formulation where partial a priori landmark information is built into the SLAM problem. Incorporating a priori relative constraints is motivated by the desire to avoid drawbacks of global constraints and to reduce uncertainty in the overall map and pose estimates. First, a Relative Deterministic-Constrained SLAM (RDC-SLAM) method is presented, where a Lagrange multiplier term is added to the cost function of the standard graph-based SLAM method, realizing a new deterministic-constrained least squares solution. Next, this method is extended to incorporate probabilistic constraints and is solved using chance-constrained optimization for a more robust least square solution, leading to Relative Probabilistic-Constrained SLAM (RPC-SLAM). Both RDC-SLAM and RPC-SLAM are tested within a Monte-Carlo framework using a 2D dataset. It is shown that the RPC-SLAM framework outperforms the other methods considered when landmark initialization is poor.
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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 it