Abstract
In the foot-mounted inertial pedestrian navigation system, the zero-velocity update (ZUPT) algorithm is an efficient way to bound the inertial error propagation. Therefore, a reliable and accurate zero-velocity detector (ZVD) that adapts to all kinds of locomotion and scenarios plays a vital role in achieving high-precision and long-term pedestrian navigation. The classical threshold-based ZVDs are susceptible to failures during dynamic locomotion due to the fixed threshold. Recent machine-learning-based ZVDs need a huge amount of data to support the model training and their generalization is limited in new testing scenarios. In this paper, we propose a novel adaptive ZVD using the optimal interval estimation. Two filters are used to process the angular rate, aiming at determining a gait cycle. In a gait cycle, the acceleration is mapped to the search space by a special convex function. Based on the features of the data in the search space, a zero-velocity benchmark is calculated for the following interval estimation. The zero-velocity benchmark and the hierarchical iterative search are used to estimate the optimal zero-velocity interval (ZVI). The experiments demonstrate the effectiveness and adaptability of this novel ZVD.
Highlights
The widespread use of the Global Navigation Satellite System (GNSS) enables localization to be available to general public
Inertial sensor has the advantage of measuring self-motion in all environments, which means that inertial navigation owns the excellent ability to enable autonomous navigation in any given environments [5]
To improve the adaptability and robustness of zero-velocity update (ZUPT), we propose a novel optimal-interval-estimation-based zero-velocity detector (ZVD) (OIE-ZVD) in this work
Summary
The widespread use of the Global Navigation Satellite System (GNSS) enables localization to be available to general public. The navigation information of GNSS is unavailable in the indoor scenarios due to the problem of signal absorption and spurious reflections [1]. The infrastructuredependent localization systems, such as Wi-Fi, UWB, GSM and iBeacon, can provide accurate navigation information by the arrangement of the indoor environments in advance [2]–[4]. These systems are inflexible in real usages, and cannot realize autonomous navigation for users in new environments. Inertial sensor has the advantage of measuring self-motion in all environments, which means that inertial navigation owns the excellent ability to enable autonomous navigation in any given environments [5]
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