Enhancing Smartphone-based Pedestrian Positioning: Using Factor Graph Optimization with Indoor/Outdoor Detection for 3DMA GNSS/Visual-Inertial State Estimation

This paper explores the pervasive challenges of pedestrian positioning using smartphones in densely populated urban environments where Global Navigation Satellite System (GNSS) signals are inaccessible, for example, in indoor areas. Existing sensor-based positioning methods, such as inertial navigation systems (INS), GNSS, and visual-inertial odometry (VIO), suffer from inherent restrictions that compromise the accuracy and reliability of the positioning performance. An approach based on machine learning is proposed to address these limitations, employing the Support Vector Machine (SVM) algorithm to accurately distinguish indoor/outdoor (IO) based on the measurement of GNSS. The proposed approach in this study seamlessly incorporates 3D mapping aided (3DMA) GNSS measurements and localized estimations derived by VIO via factor graph optimization (FGO), complemented by an IO detection switch, to achieve accurate pose estimation and effectively eliminate global drift. The system's effectiveness and robustness are rigorously assessed through comprehensive extensive real-life experiments, with an average reduction of 4 meters, leading to noteworthy and statistically significant findings. Received: 1 April 2024 | Revised: 13 September 2024 | Accepted: 26 October 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The source code that supports the findings of this study is openly available in 3DMAGNSSVINS-IOFGO at https://github.com/queenie-ho/3DMAGNSSVINS-IOFGO. Author Contribution Statement Hiu-Yi Ho and Hoi-Fung Ng: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Weisong Wen: Conceptualization, Resources, Writing - review & editing, Supervision, Project administration, Funding acquisition. Yanlei Gu: Resources, Writing - review & editing, Writing - review & editing. Li-Ta Hsu: Conceptualization, Resources, Writing - review & editing, Supervision, Project administration, Funding acquisition.