Smartphone Level Indoor/Outdoor Ubiquitous Pedestrian Positioning 3dma GNSS/Vins Integration Using Fgo

Abstract

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.

Enhanced Seamless Indoor–Outdoor Tracking Using Time Series of GNSS Positioning Errors

“…The advantage of using GNSS-based positioning is that it can be applied to all people moving in an outdoor space [4]. The problem arises when people move from outdoors to indoors because GNSS-based systems degrade their performance in these environments [5][6][7]. This occurs because GNSS signals cannot penetrate indoor spaces.…”