Machine Learning-Based Zero-Velocity Detection for Inertial Pedestrian Navigation

Kone, Yacouba; Zhu, Ni; Renaudin, Valérie; Ortiz, Miguel

doi:10.1109/jsen.2020.2999863

Cited by 38 publications

(16 citation statements)

References 30 publications

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“…Zero-velocity detection formulated as likelihood-ratio test [26] Unpublished DARPA project on foot-mounted inertial navigation [4] Bayesian formulation of zero-velocity detection [59] Robust zero-velocity detection [64], [65] Data-driven zero-velocity detection [66]- [71] Heuristic quasi ZUPTs [5]- [13] Heuristic zero-velocity detection [5]- [13], [29]- [34] Fig. 6.…”

Section: Why Is There No One Thresholdmentioning

confidence: 99%

Fifteen Years of Progress at Zero Velocity: A Review

Wahlström

Skog

2021

IEEE Sensors J.

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Fifteen years have passed since the publication of Foxlin's seminal paper "Pedestrian tracking with shoe-mounted inertial sensors". In addition to popularizing the zero-velocity update, Foxlin also hinted that the optimal parameter tuning of the zero-velocity detector is dependent on, for example, the user's gait speed. As demonstrated by the recent influx of related studies, the question of how to properly design a robust zerovelocity detector is still an open research question. In this review, we first recount the history of foot-mounted inertial navigation and characterize the main sources of error, thereby motivating the need for a robust solution. Following this, we systematically analyze current approaches to robust zero-velocity detection, while categorizing public code and data. The article concludes with a discussion on commercialization along with guidance for future research.

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Section: Why Is There No One Thresholdmentioning

confidence: 99%

Fifteen Years of Progress at Zero Velocity: A Review

Wahlström

Skog

2021

IEEE Sensors J.

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“…This accuracy in INS is defined as the distance between the last position estimated and the last actual position, with respect to the total distance traveled. A similar application can be found in [153], in which the authors used RF and gradient boost to develop an adaptable solution to ZUPT. They improved the accuracy and computational cost in comparison to the DL method.…”

Section: Machine Learning In Inertial Navigation Systemsmentioning

confidence: 99%

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

et al. 2021

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With the popularization of machine learning (ML) techniques and the increased chipset's performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.

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“…The idea behind PDR is to use the inertial signals to determine a person's stride by keeping track of when the feet hit the ground, thus limiting error growth by providing a measure of position/orientation displacement without the need to integrate the inertial signals themselves. The event pertaining to when a foot hits the ground, known as a zero-velocity update, can be tracked using an analytical [36,37] or a data-driven model [38][39][40]. Additionally, the stride lengths themselves can be determined analytically [41] or via a data-driven approach [42].…”

Section: Related Workmentioning

confidence: 99%

Localization of Biobotic Insects Using Low-Cost Inertial Measurement Units

Cole

Bozkurt

Lobatón

2020

Sensors

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Disaster robotics is a growing field that is concerned with the design and development of robots for disaster response and disaster recovery. These robots assist first responders by performing tasks that are impractical or impossible for humans. Unfortunately, current disaster robots usually lack the maneuverability to efficiently traverse these areas, which often necessitate extreme navigational capabilities, such as centimeter-scale clearance. Recent work has shown that it is possible to control the locomotion of insects such as the Madagascar hissing cockroach (Gromphadorhina portentosa) through bioelectrical stimulation of their neuro-mechanical system. This provides access to a novel agent that can traverse areas that are inaccessible to traditional robots. In this paper, we present a data-driven inertial navigation system that is capable of localizing cockroaches in areas where GPS is not available. We pose the navigation problem as a two-point boundary-value problem where the goal is to reconstruct a cockroach’s trajectory between the starting and ending states, which are assumed to be known. We validated our technique using nine trials that were conducted in a circular arena using a biobotic agent equipped with a thorax-mounted, low-cost inertial measurement unit. Results show that we can achieve centimeter-level accuracy. This is accomplished by estimating the cockroach’s velocity—using regression models that have been trained to estimate the speed and heading from the inertial signals themselves—and solving an optimization problem so that the boundary-value constraints are satisfied.

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Machine Learning-Based Zero-Velocity Detection for Inertial Pedestrian Navigation

Cited by 38 publications

References 30 publications

Fifteen Years of Progress at Zero Velocity: A Review

Fifteen Years of Progress at Zero Velocity: A Review

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

Localization of Biobotic Insects Using Low-Cost Inertial Measurement Units

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