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Sensors 2017, 17(12), 2795; https://doi.org/10.3390/s17122795

A Robust Indoor/Outdoor Navigation Filter Fusing Data from Vision and Magneto-Inertial Measurement Unit

1
Computer Vision R and D department, Sysnav, 57 rue de Montigny, 27200 Vernon, France
2
Department of Information Processing and Systems, ONERA, the French Aerospace Lab, Chemin de la Hunière, 91120 Palaiseau, France
*
Author to whom correspondence should be addressed.
Received: 31 October 2017 / Revised: 29 November 2017 / Accepted: 30 November 2017 / Published: 4 December 2017
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Abstract

Visual-inertial Navigation Systems (VINS) are nowadays used for robotic or augmented reality applications. They aim to compute the motion of the robot or the pedestrian in an environment that is unknown and does not have specific localization infrastructure. Because of the low quality of inertial sensors that can be used reasonably for these two applications, state of the art VINS rely heavily on the visual information to correct at high frequency the drift of inertial sensors integration. These methods struggle when environment does not provide usable visual features, such than in low-light of texture-less areas. In the last few years, some work have been focused on using an array of magnetometers to exploit opportunistic stationary magnetic disturbances available indoor in order to deduce a velocity. This led to Magneto-inertial Dead-reckoning (MI-DR) systems that show interesting performance in their nominal conditions, even if they can be defeated when the local magnetic gradient is too low, for example outdoor. We propose in this work to fuse the information from a monocular camera with the MI-DR technique to increase the robustness of both traditional VINS and MI-DR itself. We use an inverse square root filter inspired by the MSCKF algorithm and describe its structure thoroughly in this paper. We show navigation results on a real dataset captured by a sensor fusing a commercial-grade camera with our custom MIMU (Magneto-inertial Measurment Unit) sensor. The fused estimate demonstrates higher robustness compared to pure VINS estimate, specially in areas where vision is non informative. These results could ultimately increase the working domain of mobile augmented reality systems. View Full-Text
Keywords: Visual-inertial Navigation Systems (VINS); Magneto-inertial Dead reckoning (MI-DR); vision-based navigation; IMU navigation; MSCKF; error-state kalman filter Visual-inertial Navigation Systems (VINS); Magneto-inertial Dead reckoning (MI-DR); vision-based navigation; IMU navigation; MSCKF; error-state kalman filter
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Caruso, D.; Eudes, A.; Sanfourche, M.; Vissière, D.; Le Besnerais, G. A Robust Indoor/Outdoor Navigation Filter Fusing Data from Vision and Magneto-Inertial Measurement Unit. Sensors 2017, 17, 2795.

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