A Tightly-Integrated Magnetic-Field aided Inertial Navigation System

Abstract

A tightly integrated magnetic-field aided inertial navigation system is presented. The system uses a magnetometer sensor array to measure spatial variations in the local magneticfield. The variations in the field are -via a recursively updated polynomial magnetic-field model -mapped into displacement and orientation changes of the array, which in turn are used to aid the inertial navigation system. Simulation results show that the resulting navigation system has three orders of magnitude lower position error at the end of a 40 seconds trajectory as compared to a standalone inertial navigation system. Thus, the proposed navigation solution has the potential to solve one of the key challenges faced with current magnetic-field simultaneous localization and mapping (SLAM) systems -the very limited allowable length of the exploration phase during which unvisited areas are mapped.

MAINS: A Magnetic Field Aided Inertial Navigation System for Indoor Positioning

“…The contribution of this work is two-fold. Firstly, it extends the groundwork laid out in [24] by providing a thorough derivation and a comprehensive exposition of the proposed algorithm. Additionally, the performance of the proposed algorithm using real-world data was assessed and benchmarked against the state-of-the-art.…”

“…The model-based odometry approach was further explored in [2], where it was used to estimate both the translation and orientation change of the array. In the subsequent work [24], the authors included the magnetic field model in the state-space description of an INS system and developed a tightly-integrated magnetic-field-aided INS. Simulation results showed that it has a much slower drift rate than stand-alone INS.…”

Dynamic rEvolution : Adaptive state estimation via Gaussian processes and iterative filtering

“…State estimation is ubiquitous not only for automatic control but for virtually every area of science and engineering. It is a necessary component for target tracking [6,7], motion planning [8,9], simultaneous localization and mapping (slam) [10], sensor management [11], model-based control such as model predictive control [12], inertial navigation [13], and more. Regardless of the application, state estimation provides us with a way of extracting additional information from various measurements, e.g., we can succinctly express that velocity is the time derivative of the position, enabling us to estimate both the position as well as the velocity from only measurements of the position.…”

Magneto-Inertial Dead-Reckoning Navigation with Walk Dynamic Model in Indoor Environment

“…MIDR WDM-MIDR Average gradient (G/m) Low gradient 6 6.9% 0.9% 0.02 High gradient 0.7% 0.7% 0.13 velocity stationary state on the ground. This inaccuracy is especially emphasized if the person walks very quickly -i.e.…”