LAYOUT SLAM WITH MODEL BASED LOOP CLOSURE FOR 3D INDOOR CORRIDOR RECONSTRUCTION
Bibliographic record
Abstract
Abstract. In this paper, we extend a recently proposed visual Simultaneous Localization and Mapping (SLAM) techniques, known as Layout SLAM, to make it robust against error accumulations, abrupt changes of camera orientation and miss-association of newly visited parts of the scene to the previously visited landmarks. To do so, we present a novel technique of loop closing based on layout model matching; i.e., both model information (topology and geometry of reconstructed models) and image information (photometric features) are used to address a loop-closure detection. The advantages of using the layout-related information in the proposed loop-closing technique are twofold. First, it imposes a metric constraint on the global map consistency and, thus, adjusts the mapping scale drifts. Second, it can reduce matching ambiguity in the context of indoor corridors, where the scene is homogenously textured and extracting sufficient amount of distinguishable point features is a challenging task. To test the impact of the proposed technique on the performance of Layout SLAM, we have performed the experiments on wide-angle videos captured by a handheld camera. This dataset was collected from the indoor corridors of a building at York University. The obtained results demonstrate that the proposed method successfully detects the instances of loops while producing very limited trajectory errors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".