Abstract
This paper discusses ubiquitous smartphone pedestrian positioning challenges in urban canyons and GNSS-denied areas such as indoor spaces. Existing sensor-based techniques, including GNSS, INS, and VIO, have limitations that affect positioning accuracy and reliability. A machine learning-based approach is suggested to employ Support Vector Machine (SVM) to classify indoor/outdoor (IO) detection using GNSS measurement data. The proposed system integrates local estimates on VIO and 3D mapping aided (3DMA) GNSS measurements using Factor Graph Optimization (FGO) with an IO detection switch to estimate precise pose and eliminate global drift. The effectiveness of the system is evaluated through real-world experiments that produce notable outcomes.
| Original language | English |
|---|---|
| Pages (from-to) | 175-182 |
| Number of pages | 8 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 48 |
| Issue number | 1/W1-2023 |
| DOIs | |
| Publication status | Published - 25 May 2023 |
| Event | 12th International Symposium on Mobile Mapping Technology, MMT 2023 - Padua, Italy Duration: 24 May 2023 → 26 May 2023 |
Keywords
- 3DMA GNSS
- FGO
- IO
- Pedestrian Positioning
- Sensor Integration
- Smartphone
- Ubiquitous
- VINS
ASJC Scopus subject areas
- Information Systems
- Geography, Planning and Development