Visual indoor positioning with a single camera using PnP
Bibliographic record
Abstract
This paper introduces an accurate and inexpensive method for localizing a calibrated monocular camera in 3D indoor environments. The objective of this work is to localize in 6 degrees-of-freedom (6 DOF) in the presence of a 3D map that contains 3D point clouds co-registered with intensity information. This is done by solving the Perspective-n-Point (PnP) problem to accurately compute the camera location in 6 DOF. An efficient data structure is used to store a large set of point clouds co-registered with intensity information, image features, and transformations between the images. This data structure, referred to as the feature database, is implemented such that it retrieves a match for a query image efficiently. Thus the overall process of localization in 6 DOF becomes a real-time process with high efficiency and accuracy. Our technique was tested with two ground truth data sets of indoor environments, an office and a laboratory. The experimental results show the accuracy and the efficiency of our technique, with an average localization error of less than 10 mm from the ground truth in both environments. In addition, localization results on query images obtained using two different cameras in four different environments are presented. This demonstrates that any type of monocular camera may be used during localization, as long as a sufficient number of environmental features can be extracted from the query images.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".