Using BIM and Sensing Mats to Improve IMU-based Indoor Positioning Accuracy
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".