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 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 | 0.001 |
| 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".