An Enhanced Smartphone GNSS/MEMS-IMU Integration Seamless Positioning Method in Urban Environments

Smartphones, due to their ubiquity, portability, and low cost, have become the primary devices for navigation and location services, while access to the global navigation satellite system (GNSS) and micro-electro-mechanical systems (MEMS)-inertial measurement unit (IMU) observations has further stimulated research into seamless road-level positioning using smartphones. However, in urban environments, GNSS observations are susceptible to nonline-of-sight (NLOS) signals, leading to numerous outliers and substantial errors in GNSS/MEMS-IMU-integrated positioning. This study proposes a fisheye camera-assisted GNSS/MEMS-IMU sliding window factor graph fusion positioning method. The scheme employs an external fisheye camera attached to the smartphone for NLOS detection, re-weights line-of-sight (LOS) and NLOS observations using our comprehensive weighting model, and utilizes sliding window marginalization to optimize the GNSS and MEMS-IMU data. The experimental results show that the proposed method enhances 3-D positioning accuracy by 46.7%, 50.7%, and 58.4% in the light urban, middle urban, and dense urban kinematic environments, respectively, compared with the conventional factor graph optimization (FGO) method. Additionally, with respect to the conventional batch optimization method, the proposed method also reduces the total computation time by more than 60%. This work presents a novel approach for enhancing road-level real-time positioning to lane-level real-time positioning, based on low-cost smartphones, thus would be of extensive application potential in the field of smartphone urban seamless positioning.