Abstract
Pedestrian navigation systems could serve as a good supplement for other navigation methods or for extending navigation into areas where other navigation systems are invalid. Due to the accumulation of inertial sensing errors, foot-mounted inertial-sensor-based pedestrian navigation systems (PNSs) suffer from drift, especially heading drift. To mitigate heading drift, considering the complexity of human motion and the environment, we introduce a novel hybrid framework that integrates a foot-state classifier that triggers the zero-velocity update (ZUPT) algorithm, zero-angular-rate update (ZARU) algorithm, and a state lock, a magnetic disturbance detector, a human-motion-classifier-aided adaptive fusion module (AFM) that outputs an adaptive heading error measurement by fusing heuristic and magnetic algorithms rather than simply switching them, and an error-state Kalman filter (ESKF) that estimates the optimal systematic error. The validation datasets include a Vicon loop dataset that spans 324.3 m in a single room for approximately 300 s and challenging walking datasets that cover large indoor and outdoor environments with a total distance of 12.98 km. A total of five different frameworks with different heading drift correction methods, including the proposed framework, were validated on these datasets, which demonstrated that our proposed ZUPT–ZARU–AFM–ESKF-aided PNS outperforms other frameworks and clearly mitigates heading drift.
Highlights
Localization systems have great benefits for numerous applications, such as monitoring daily action for the aged, assisting visually impaired people, and localizing first responders
To mitigate the heading drift and improve the positioning performance of the pedestrian navigation systems (PNSs), we introduce a hybrid framework that integrates a foot-state classifier (FSC) that triggers the zero-velocity update (ZUPT) algorithm, the zeroangular-rate update (ZARU) algorithm, and the state lock, an magnetic disturbance detector (MDD) and a human motion classifier (HMC)-aided adaptive fusion module (AFM) that fuses the electronic compass (EC) and heuristic drift reduction (HDR)
We outline and present the validation of our proposed framework
Summary
Localization systems have great benefits for numerous applications, such as monitoring daily action for the aged, assisting visually impaired people, and localizing first responders. Pedestrian navigation technology based on wearable inertial measurement units (IMUs) has attracted increasing interest from researchers because of its low requirements for external positioning beacons, light weight, and low cost. The technology can solve positioning problems in environments in which satellite signals are denied. These localization systems are implemented using head-mounted IMUs [1], chest-mounted IMUs [2], waist-mounted IMUs [3], handheld. One is implemented by computer processing of the bottom sensor data [2]. Another way is implemented directly by a bottom-embedded system [8].
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