Live Demonstration: Double SLERP Gravity-Magnetic Vector (GMV-D) orientation correction in a MARG sensor

Ratchatanantakit, Neeranut; O-larnnithipong, Nonnarit; Sonchan, Pontakorn; Adjouadi, Malek; Barreto, Armando

doi:10.1109/sensors47087.2021.9639677

Cited by 3 publications

(1 citation statement)

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“…Under the assumption that the large ferromagnetic objects around the human body being monitored will not be in constant and rapid motion, we previously sought to model the distortion of the geomagnetic field as a property of three-dimensional space. In a previous implementation of a system for tracking the position and configuration of the hand of a human subject [44,45], the MARG was used in conjunction with a 3-IR camera system (OptiTrack V-120 Trio. OptiTrack is a division of NaturalPoint, Inc., Corvallis, OR, USA), that provided X, Y, and Z position coordinates for the MARG.…”

Section: Original Formulationsmentioning

confidence: 99%

Robust Orientation Estimation from MEMS Magnetic, Angular Rate, and Gravity (MARG) Modules for Human–Computer Interaction

Sonchan,

Ratchatanantakit,

O-Larnnithipong

et al. 2024

Micromachines

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While the availability of low-cost micro electro-mechanical systems (MEMS) accelerometers, gyroscopes, and magnetometers initially seemed to promise the possibility of using them to easily track the position and orientation of virtually any object that they could be attached to, this promise has not yet been fulfilled. Navigation-grade accelerometers and gyroscopes have long been the basis for tracking ships and aircraft, but the signals from low-cost MEMS accelerometers and gyroscopes are still orders of magnitude poorer in quality (e.g., bias stability). Therefore, the applications of MEMS inertial measurement units (IMUs), containing tri-axial accelerometers and gyroscopes, are currently not as extensive as they were expected to be. Even the addition of MEMS tri-axial magnetometers, to conform magnetic, angular rate, and gravity (MARG) sensor modules, has not fully overcome the challenges involved in using these modules for long-term orientation estimation, which would be of great benefit for the tracking of human–computer hand-held controllers or tracking of Internet-Of-Things (IoT) devices. Here, we present an algorithm, GMVDμK (or simply GMVDK), that aims at taking full advantage of all the signals available from a MARG module to robustly estimate its orientation, while preventing damaging overcorrections, within the context of a human–computer interaction application. Through experimental comparison, we show that GMVDK is more robust to magnetic disturbances than three other MARG orientation estimation algorithms in representative trials.

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Section: Original Formulationsmentioning

confidence: 99%