A Black-Box Positioning Solution for Effortless Integration in Wearable Navigation

Abstract:Accurate localization is critical for a wide range of applications, including navigation, augmented reality, safety management, and healthcare. However, achieving accurate positioning remains challenging, especially in GPS-degraded environments. Pedestrian Dead Reckoning (PDR), which estimates position based on inertial sensor data, offers a promising alternative but typically demands substantial effort in algorithm design and system integration. This complexity often leads to prolonged development timelines and elevated costs. To address these challenges, we present the PDR Sensor (PDRS), a plug-and-play, black-box PDR module optimized for seamless integration into wearable and IoT platforms. PDRS integrates a microprocessor and an inertial measurement unit (IMU) within a compact, low-power module. It executes real-time sensor fusion onboard, providing position updates via standard interfaces (I2C, SPI, UART). Experimental evaluations demonstrate its high robustness, achieving a step detection error of just 0.16% over 1250 steps and a traveled distance error of 1.87% under diverse conditions. The system further exhibits low latency (2.5 ms) and modest power consumption (52.35 mA). Unlike conventional studies, the PDRS leverages a holistic co-design of hardware and software to enhance performance and integration aspects, simplifying adoption without requiring deep expertise. By lowering technical barriers, PDRS enables developers and researchers to prioritize innovation in IoT and wearable technologies, accelerating prototyping, reducing development time, and advancing industry progress. Future work aims to enhance accuracy, reduce power usage and module size, and integrate AI-driven techniques to better mitigate drift.

Robotics (cs.RO); Hardware Architecture (cs.AR)

arXiv:2610.21353 [cs.RO]

(or arXiv:2610.21353v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.21353 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1109/SMC58881.2025.11342779 Focus to learn more DOI(s) linking to related resources

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