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Improving Visual-Inertial Navigation Using Stationary Environmental Magnetic Disturbances

Abstract : This thesis addresses the issue of positioning in 6-DOF that arises from augmented reality applications and focuses on embedded sensors based solutions.Nowadays, the performance reached by visual-inertial navigation systems is starting to be adequate for AR applications. Nonetheless, those systems are based on position correction from visual sensors involved at a relatively high frequency to mitigate the quick drift of low-cost inertial sensors. This is a problem when the visual environment is unfavorable.In parallel, recent works have shown it was feasible to leverage magnetic field to reduce inertial integration drift thanks to a new type of low-cost sensor, which includes – in addition to the accelerometers and gyrometers – a network of magnetometers. Yet, this magnetic approach for dead-reckoning fails if stationarity and non-uniformity hypothesis on the magnetic field are unfulfilled in the vicinity of the sensor.We develop a robust dead-reckoning solution combining simultaneously information from all these sources: magnetic, visual, and inertial sensor. We present several approaches to solve for the fusion problem, using either filtering or non-linear optimization paradigm and we develop an efficient way to use magnetic error term in a classical bundle adjustment that was inspired from already used idea for inertial terms. We evaluate the performance of these estimators on data from real sensors. We demonstrate the benefits of the fusion compared to visual-inertial and magneto-inertial solutions. Finally, we study theoretical properties of the estimators that are linked to invariance theory.
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Submitted on : Wednesday, October 3, 2018 - 11:52:09 AM
Last modification on : Wednesday, October 14, 2020 - 4:10:31 AM
Long-term archiving on: : Friday, January 4, 2019 - 1:52:44 PM


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  • HAL Id : tel-01886847, version 1



David Caruso. Improving Visual-Inertial Navigation Using Stationary Environmental Magnetic Disturbances. Computer Vision and Pattern Recognition [cs.CV]. Université Paris Saclay (COmUE), 2018. English. ⟨NNT : 2018SACLS133⟩. ⟨tel-01886847⟩



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