LocMe: Human locomotion and map exploitation based indoor localization
Bibliographic record
Abstract
State-of-the-art indoor localization techniques can reach high localization accuracy, but rely on widely deployed infrastructure which may be absent in less developed regions. To achieve infrastructure-free indoor localization, researchers have proposed to use the inertial sensors on the mobile devices to update the users potential locations by walking steps. Such methods are known to accumulate errors and degrade drastically over time. To compensate for it, either a wall-constraint that eliminates possible steps going through the walls, or landmarks correspond to the special user activities, are usually employed. However, these approaches still suffer from slow convergence and cannot detect floor changes automatically without the information from other users. In this paper, we propose LocMe, an indoor localization service based on human locomotion detection and map exploitation. By synergizing both the locomotion-constraint and wall-constraint, LocMe can significantly increase the converging speed of localization and automatically detect the floor changes, with negligible extra complexity. Our field tests show that LocMe can achieve a median localization error of 1.1 m, which is over 68% lower than the localization algorithm with only the wall-constraint in the same test condition.
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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".