融合多源信号实现室内外无缝定位，对比三种算法优劣。
Seamless Outdoor-Indoor Pedestrian Positioning System with GNSS/UWB/IMU Fusion: A Comparison of EKF, FGO, and PF
- 用惯性导航做运动主干，结合GNSS、UWB提供绝对位置修正。
- 室内外切换时定位误差控制在1.2米以内，城市环境性能更稳定。
- 适合可穿戴设备实时运行，适用于智能导航与无人配送场景。
跨室内外的精准连续行人定位仍具挑战性，因GNSS、UWB与惯性PDR各自在信号遮挡、多径效应和漂移下表现脆弱。本文提出统一的GNSS/UWB/IMU融合框架，并对三种概率后端方法进行对照：误差状态扩展卡尔曼滤波（ESKF）、滑动窗口因子图优化（FGO）与粒子滤波（PF）。系统采用佩戴于胸部的IMU-based PDR作为运动主干，室外依赖GNSS提供绝对更新，室内则使用UWB。为提升过渡鲁棒性并缓解城市中GNSS退化问题，引入基于OpenStreetMap建筑轮廓的轻量级地图可行性约束，将多数建筑内部设为不可通行区域，仅允许在指定配备UWB的建筑内移动。框架基于ROS 2实现，可在可穿戴平台实时运行，并通过Foxglove可视化。评估涵盖三种场景：室内（UWB+PDR）、室外（GNSS+PDR）与无缝室内外过渡（GNSS+UWB+PDR）。结果表明，在本实现中ESKF整体性能最一致。
原文摘要 · Abstract (English)
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.
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