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.
Highlights
And except stated otherwise we use the following symbols: p for the translational part of the body pose, R for its rotational part. v for the velocity, b a and b g for inertial sensor bias, B for the magnetic field, ∇B for its 3 × 3 gradient matrix. ω is used for the rotational speed and a for the specific acceleration. 3D landmark position are noted with the letter l and these observation into an image with the letter o
The trajectory estimated by Magneto-inertial Dead-reckoning (MI-DR) is very close to the others until some point in the outdoor part—note that the outdoor part corresponds to the weak gradient part of the trajectory as depicted on Figure 6b
This work presented a filter to fuse information from a magnetometer array with other sensors traditionally used in Visual-inertial Navigation Systems (VINS)
Summary
Infrastructure-less navigation and positioning in indoor location is a technical prerequisite for numerous industrial and consumer applications: ranging from lone worker safety in industrial facilities, to augmented reality. It remains an open challenge to efficiently and reliably combine embedded sensors to reconstruct a position or a trajectory. We address this challenge restricting ourselves to the following sensors: MEMS gyroscopes, accelerometers, magnetometers and a standard industrial vision camera. We motivate this choice by the fact these sensors are cheap and can be embedded in a wearable form factor, which makes this combination. VINS (Visual-Inertial Navigation Systems) literature, showed recently tremendous progress in the past few years
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