Model-based Dynamic Pose Graph SLAM in Unstructured Dynamic Environments
Bibliographic record
Abstract
Navigation in dynamic environments is a challenge for autonomous vehicles operating without prior maps or global position references. This poses high risk to vehicles that perform scientific studies and monitoring missions in marine Arctic environments characterized by slowly moving sea ice with few truly static landmarks. Whereas mature simultaneous localization and mapping (SLAM) approaches assume a static environment, this work extends pose graph SLAM to spatiotemporally evolving environments. A novel model-based dynamic factor is proposed to capture a landmark's state transition model - whether the state be kinematic, appearance or otherwise. The structure of the state transition model is assumed to be known a priori, while the parameters are estimated on-line. Expectation maximization is used to avoid adding variables to the graph. Proof-of-concept results are shown in small- and medium-scale simulation, and small-scale laboratory environments for a small quadrotor. Preliminary laboratory validation results shows the effect of mechanical limitations of the quadrotor platform and increased uncertainties associated with the model-based dynamic factors on the SLAM estimate. Simulation results are encouraging for the application of model-based dynamic factors to dynamic landmarks with a constant-velocity kinematic model.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".