🤖 Humanoid 🦾 Industrial & Cobot 🚚 AGV / AMR 🐕 Quadruped ⚙️ Reducers · Servos · Sensors 🚁 Drones & Autonomy 🧠 Embodied AI
Robos News
Robotics

ForestBack: Breadcrumb-Based Pedestrian Dead Reckoning for Infrastructure-Free Return Navigation

arXiv:2606.14421v1 Announce Type: new Abstract: 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 requir

ForestBack: Breadcrumb-Based Pedestrian Dead Reckoning for Infrastructure-Free Return Navigation

Published June 15, 2026 · Category: Robotics

Overview

arXiv:2606.14421v1 Announce Type: new Abstract: 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.

Source

Originally published at arxiv.org.

Related Articles

CD
Robos News Newsroom

Robos News covers markets, crypto and commodities for Asia & the Middle East — tier-1 desk research, AI-driven analysis, institutional-grade data. Tip our newsroom: [email protected]

Email the newsroom →
Disclaimer: This article is for informational purposes only and does not constitute investment advice. Data may be delayed up to 15 minutes. Past performance is not indicative of future results. Consult a licensed financial advisor before making investment decisions.

Related Stories

More from News →