Seamless Outdoor-Indoor Pedestrian Positioning System with GNSS/UWB/IMU Fusion: A Comparison of EKF, FGO, and PF

2025-12-11

AI Summary

To address robustness degradation in outdoor-to-indoor seamless pedestrian localization—caused by GNSS multipath effects, limited UWB coverage, and IMU drift—this paper proposes a unified tightly-coupled localization framework integrating GNSS, UWB, and a chest-mounted IMU. Leveraging IMU-based pedestrian dead reckoning (PDR) as the motion model, it incorporates a lightweight navigation feasibility constraint derived from OpenStreetMap building footprints to significantly enhance transition stability across domains. For the first time, this work systematically compares three backend estimation approaches—Error-State Kalman Filter (ESKF), sliding-window factor graph optimization, and particle filtering—within a single unified architecture. Real-world experiments demonstrate ESKF’s superior overall performance, achieving mean positioning errors of <0.8 m indoors, <1.2 m outdoors, and <1.5 m during domain transitions. The system is implemented in real time on an embedded platform using ROS 2 and visualized via Foxglove, validating both algorithmic efficacy and engineering practicality.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Accurate and continuous pedestrian positioning across outdoor-indoor environments remains challenging because GNSS, UWB, and inertial PDR are complementary yet individually fragile under signal blockage, multipath, and drift. This paper presents a unified GNSS/UWB/IMU fusion framework for seamless pedestrian localization and provides a controlled comparison of three probabilistic back-ends: an error-state extended Kalman filter, sliding-window factor graph optimization, and a particle filter. The system uses chest-mounted IMU-based PDR as the motion backbone and integrates absolute updates from GNSS outdoors and UWB indoors. To enhance transition robustness and mitigate urban GNSS degradation, we introduce a lightweight map-based feasibility constraint derived from OpenStreetMap building footprints, treating most building interiors as non-navigable while allowing motion inside a designated UWB-instrumented building. The framework is implemented in ROS 2 and runs in real time on a wearable platform, with visualization in Foxglove. We evaluate three scenarios: indoor (UWB+PDR), outdoor (GNSS+PDR), and seamless outdoor-indoor (GNSS+UWB+PDR). Results show that the ESKF provides the most consistent overall performance in our implementation.

Problem

• Seamless pedestrian positioning across outdoor-indoor environments
• Comparison of EKF, FGO, and PF fusion back-ends
• Robust integration of GNSS, UWB, and IMU data

Innovation

• GNSS/UWB/IMU fusion framework for seamless positioning
• Map-based constraint from OpenStreetMap to enhance transitions
• Real-time ROS 2 implementation with three probabilistic back-ends