ForestBack：基于面包屑的步行者航位推算实现无基础设施返回导航
ForestBack: Breadcrumb-Based Pedestrian Dead Reckoning for Infrastructure-Free Return Navigation
AI总结：
提出ForestBack框架，通过面包屑式步行者航位推算（PDR）在无GPS/基础设施环境中记录路径并生成反向引导，实验显示轨迹RMSE降低15.76%。
摘要：
在GPS受限且外部定位基础设施可能不可用或不可靠的环境中，可靠的返回导航仍然是一个重要挑战。本文提出ForestBack，一种基于面包屑式步行者航位推算（PDR）的无基础设施行人返回导航框架。该系统将用户的行走路线记录为一系列可逆的面包屑节点，并在无需GPS、Wi-Fi、蓝牙信标或预装基础设施的情况下生成反向路径引导。ForestBack集成了基于加速度的步态检测、自适应步长估计、磁力计辅助航向估计、气压高度校正以及双向面包屑路径重建。该系统使用一条包含五个检查点的室内避障路线进行评估，用户围绕一个中心障碍物导航。评估使用了包含36次行走试验和42,474个时间序列样本的数据集，包括IMU信号、磁力计读数、气压变量、转弯事件标签、地面真实轨迹、基线PDR输出、提出的ForestBack输出以及功率相关测量。实验结果表明，与传统PDR相比，ForestBack将平均RMSE从1.129米降低到0.965米，提高了15.76%。平均最终位置误差从1.781米降低到1.388米，而转弯事件检测一致性达到约99.90%。这些结果表明，ForestBack在避障场景中改善了轨迹重建和路径保持的返回引导。发布的数据集和分析笔记本支持可重复性以及未来对基于PDR的无基础设施返回导航系统的基准测试。
英文摘要：
Reliable return navigation remains an important challenge in GPS-denied environments where external positioning infrastructure may be unavailable or unreliable. This paper presents ForestBack, an infrastructure-free pedestrian return navigation framework based on breadcrumb-based pedestrian dead reckoning (PDR). The system records a user's walking route as a sequence of reversible breadcrumb nodes and generates reverse-path guidance without requiring GPS, Wi-Fi, Bluetooth beacons, or pre-installed infrastructure. ForestBack integrates acceleration-based step detection, adaptive step-length estimation, magnetometer-assisted heading estimation, barometric-altitude correction, and bidirectional breadcrumb path reconstruction. The system was evaluated using an indoor obstacle-avoidance route with five checkpoints, where the user navigated around a central obstacle. A dataset of 36 walking trials and 42,474 time-series samples was used for evaluation, including IMU signals, magnetometer readings, barometric variables, turn-event labels, ground-truth trajectories, baseline PDR outputs, proposed ForestBack outputs, and power-related measurements. Experimental results show that ForestBack reduced the mean RMSE from 1.129 m to 0.965 m compared with traditional PDR, corresponding to a 15.76% improvement. The mean final-position error was reduced from 1.781 m to 1.388 m, while turn-event detection consistency reached approximately 99.90%. These results indicate that ForestBack improves trajectory reconstruction and route-preserving return guidance in obstacle-avoidance scenarios. The released dataset and analysis notebook support reproducibility and future benchmarking of infrastructure-free PDR-based return navigation systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
