Object Reconstruction and Localization in Indoor Environments Using Infrastructure Sensor Node
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Indoor perception is a field that has gained traction in recent years. While there has been a significant amount of research done on outdoor perception and motion planning, the indoor environment has yet to receive similar treatment. In an indoor environment, various sensor systems have been developed to track and localize objects, each tackling a different set of challenges. In this article, we introduce a novel infrastructure sensor node (ISN) consisting of a light detection and ranging (LiDAR) along with two monocular cameras mounted on the ceiling of the hallways of our laboratory to obtain relevant information. We present a perception pipeline that uses prior 3-D point cloud registration to localize objects in real time in dynamic indoor environments. We provided a complete case study to present a work that successfully detects, registers, and localizes objects through a dynamic environment with a high degree of occlusion.
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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