Conclusion

In this article, the main technologies, in terms of sensors, for magneto-inertial navigation have been presented, followed by two recent Kalman filtering approaches for estimating navigation states, combining the sensors considered. The impact of a coupling between EKF and artificial intelligence and how these techniques can compensate/improve certain problems of this type of filtering were discussed.

It is worth noting that a number of research projects have been carried out using very low-cost magneto-inertial sensors for navigation applications, while taking into account error modeling of these sensors. These research efforts will expand in the future and aim to achieve precision navigation solutions for applications in the oil and gas industry, aerospace, pedestrian, wearable devices, unmanned aerial vehicles and autonomous cars.

Potential...