Hierarchical Primitive and Semantics aided Scan Context for place recognition using LiDAR and Monocular Image
Bibliographic record
Abstract
Indoor localization involves a challenging and essential task of recognizing places, which has been approached through multi-sensor solutions. However, methods based on a single level of features and homogenous features suffer from ambiguity and lack robustness to changes in environments and viewpoint. To address this challenge, we propose hierarchical primitive, and semantics aided scan context that uses a hierarchical feature comprising primitives and point-level features based on a coupled LiDAR and visual camera system. The proposed feature provides a combination of local and global description, incorporating their advantages while balancing their individual drawbacks. Planar primitives from both image and point clouds are detected for coarse recognition and selection of similar candidates, improving the independence of viewpoint and scenario similarity. Point descriptors, including scan context and SIFT, are then obtained for stage of fine recognition within the previous candidates. The results are evaluated using one real-world indoor dataset. Experimental results demonstrate that the proposed feature descriptor achieves accurate place recognition at the state of the art level, compared to the original scan context descriptor.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".