Multiple Binocular Cameras-Based Indoor Localization Technique Using Deep Learning and Multimodal Fusion
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, an image based indoor localization technique using multiple binocular cameras is proposed by the deep learning and multimodal fusion. First, by taking advantage of the cross-model correlations between various multimodal images for localization purpose, the obtained images are concatenated to form two new modalities: three-channel gray image and three-channel depth image. Then, a two-stream convolutional neural network (CNN) is used for multimodal feature extraction which can ensure the independent of each image modality. Moreover, a decision-level fusion rule is proposed to fuse the extracted features with the linear weight sum method. At last, in order to make use of the feature correlation between each image modality, the fused feature is extracted once again by two convolutional max-pooling blocks. The shrinkage Loss based loss function is designed to obtain the position based regression function at last. Field tests show that the proposed algorithm can obtain more accurate position estimation than other existing image based localization approaches.
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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 it