Being in Two Places at Once: Smooth Visual Path Following on Globally Inconsistent Pose Graphs
Bibliographic record
Abstract
Early work in the field of SLAM asserted that globally metrically consistent maps expressed in a single coordinate frame were necessary for autonomous operation. It has been shown previously that chain-structured and tree-structured optometric maps provide sufficient information for accurate path following. This paper extends this concept to arbitrarily connected graph structures with loop closures. We show that globally inconsistent maps may be treated as a set of locally defined Riemannian manifolds, and that this representation is sufficient for path repetition tasks. We demonstrate smooth path following on an inconsistent optometric map with loop closures, using the existing Visual Teach and Repeat (VT&R) framework for vision-in-the-loop control. Path-tracking errors are maintained within nominal values despite disparities of over 2m between the local and global representations of robot pose. Traversal of large map discontinuities is found to have no adverse effect on robot performance, allowing segments of the map to be repeated in a different order than they were trained.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".