Indoor PDR Method Based on Foot-Mounted Low-Cost IMMU
Bibliographic record
Abstract
Indoor Pedestrian Dead Reckoning (PDR) based on Inertial and Magnetic Measurement Unit (IMMU) can accurately provide the position of pedestrians, and gradually becomes popular research on indoor positioning. In this paper, a novel PDR algorithm based on low-cost IMMU is proposed, which implements PDR from four steps: step detection, gait detection, step size estimation and attitude solution. According to the pitch angle, it is judged whether a new step is generated, and gait detection algorithm based on the standard deviation of the acceleration modulus and the angular velocity threshold + the angular velocity standard deviation threshold is proposed, which is the basis of step length estimation and attitude solution. The performance of the algorithm is verified through indoor experiments. The results show that the average distance error in the indoor environment was 1.32% and the average end-to-end error was 1.21%. Therefore, this paper based on low-cost IMMU indoor PDR has great application value.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".