无需外部设施,用脚步记录反向导航路径
ForestBack: Breadcrumb-Based Pedestrian Dead Reckoning for Infrastructure-Free Return Navigation

- 用步数、方向、高度变化生成可逆的导航节点序列
- 相比传统方法定位误差降低15.76%,终点误差减少22%
- 适合野外、室内等无GPS环境下的徒步返程导航
在无GPS信号的环境中,可靠返回导航仍是重大挑战。本文提出ForestBack,一种基于足迹标记的行人航位推算(PDR)框架,无需依赖GPS、Wi-Fi、蓝牙信标或预装基础设施。系统通过惯性测量单元(IMU)实现步数检测、自适应步长估计、磁力计辅助方向估计、气压高度修正,并支持双向足迹路径重建。实验使用包含5个检查点的室内避障路线,共36次步行试验和42,474条时序数据样本进行评估,涵盖IMU信号、磁力计读数、气压变量、转弯事件标签、真实轨迹、基线PDR输出及本方案输出。结果表明,相较于传统PDR,ForestBack将平均均方根误差(RMSE)从1.129米降至0.965米(提升15.76%),终点位置误差由1.781米降至1.388米,转弯事件检测一致性达约99.90%。结果证明其在避障场景中显著提升轨迹重建与路径保持返程导航性能。公开的数据集与分析笔记支持复现与未来基准测试。
原文摘要 · Abstract (English)
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.
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