Article (584 Kb) - 
Free accessOther version: https://ieeexplore.ieee.org/document/8110606/
Highlights
More informations
Scientific Article
unige:96033 
 |
A computationally efficient framework for automatic inertial sensor calibration |
|
| Authors | ||
| Published in | IEEE Sensors Journal. 2018, vol. 18, no. 4, p. 1636-1646 | |
| Abstract | The calibration of (low-cost) inertial sensors has become increasingly important over the past years since their use has grown exponentially in many applications going from unmanned aerial vehicle navigation to 3D-animation. However, this calibration procedure is often quite problematic since the signals issued from these sensors have a complex spectral structure and the methods available to estimate the parameters of these models are either unstable, computationally intensive and/or statistically inconsistent. This paper presents a new software platform for inertial sensor calibration based on the Generalized Method of Wavelet Moments which provides a computationally efficient, flexible, user-friendly and statistically sound tool to estimate and select from a wide range of complex models. The software is developed within the open-source statistical software R and is based on C++ language allowing it to achieve high computational performance. | |
| Keywords | Generalized method of wavelet moment — Wavelet variance — Allan variance — Error modeling — Time serie — Estimation method — Micro-electro-mechanical system — Kalman filter — R software | |
| Identifiers | arXiv: 1603.05297v2 | |
| Full text | ||
| Structures | ||
| Citation (ISO format) | BALAMUTA, James et al. A computationally efficient framework for automatic inertial sensor calibration. In: IEEE Sensors Journal, 2018, vol. 18, n° 4, p. 1636-1646. https://archive-ouverte.unige.ch/unige:96033 |
112 hits 
29 downloads 
Contact an author
Update