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

TL;DR

This paper develops a unified pedestrian positioning system combining GNSS, UWB, and IMU sensors, compares three probabilistic methods, and introduces a map-based constraint to improve indoor-outdoor localization robustness.

Contribution

It presents a novel fusion framework with a real-time implementation, comparing EKF, FGO, and PF, and introduces a map-based feasibility constraint for seamless outdoor-indoor positioning.

Findings

EKF provides the most consistent performance among the tested methods.

The system achieves real-time operation on a wearable platform.

Map-based constraints improve indoor transition robustness.

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.