Indoor waypoint navigation via magnetic anomalies
Bibliographic record
Abstract
A wide assortment of technologies have been proposed to construct indoor navigation services for the blind and vision impaired. Proximity-based systems and multilateration systems have been successfully demonstrated and employed. Despite the technical success of these technologies, broad adoption has been limited due to their significant infrastructure and maintenance costs. An alternative approach utilizing the indoor magnetic signatures inherent to steel-frame buildings solves the infrastructure cost problem; in effect the existing building is the location system infrastructure. Although magnetic indoor navigation does not require the installation of dedicated hardware, the dedication of resources to produce precise survey maps of magnetic anomalies represents a further barrier to adoption. In the present work an alternative leader-follower form of waypoint-navigation system has been developed that works without surveyed magnetic maps of a site. Instead the wayfarer's magnetometer readings are compared to a pre-recorded magnetic "leader" trace containing magnetic data collected along a route and annotated with waypoint information. The goal of the navigation system is to correlate the follower's magnetometer data with the leader's to trigger audio cues at precise points along the route, thus providing location-based guidance to the user. The system should also provide early indications of off-route conditions. As part of the research effort a smartphone based application was created to record and annotate leader traces with audio and numeric data at waypoints of interest, and algorithms were developed to determine (1) when the follower reaches a waypoint and (2) when the follower goes off-route. A navigation system utilizing this technology would enable a low-cost indoor navigation system capable of replaying audio annotations at precise locations along pre-recorded routes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".