Using BIM and Sensing Mats to Improve IMU-based Indoor Positioning Accuracy
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Bibliographic record
Abstract
Using BIM and Sensing Mats to Improve IMU-based Indoor Positioning Accuracy Chia-Hsien Chen and I-Chen Wu Pages 818-823 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Currently, numerous approaches to Indoor Positioning Systems (IPSs), such as RSSI (Received Signal Strength Indication), fingerprint, PDR (Pedestrian Dead-Reckoning), and image recognition, have been developed. But each individual positioning method has unique drawbacks. In this study, we provide an IPS with a novel combined positioning method that applies Building Information Modelling (BIM) and Internet of Things (IoT). We employ an Inertial Measurement Unit (IMU) to track peoples positions. We then utilize a BIM model that has information (semantic and geometric) and a sensing mat to eliminate IMU drift error in the positioning process. The demonstration field is a research office, and test results show that the BIM based positioning constraint can effectively filter IMU cumulative error along with time; thereby, positioning accuracy can be controlled to a range of 30cm × 30cm. In sum, this paper proposes a new positioning method that compensates for the weakness of the IMU. In the future, this system can be applied to people management, such as telecare for older adults. Keywords: BIM; Indoor Positioning System; IoT; IMU; Sensing Mat DOI: https://doi.org/10.22260/ISARC2019/0110 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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