Graph-based fusion of magnetometer and learned-based inertial odometry
The 3D position estimation of pedestrians is a vital problem in the development of virtual reality, augmented reality, and the internet of things. The learning-based inertial odometry is a very potential auxiliary method of pedestrian positioning due to its low position drift and immunity to external environmental influences. However, in many cases, the drift error of the heading is still the main factor that causes the rapid divergence of the position estimated by the learning based inertial odometry. This paper proposed a graph optimization-based estimation method to fusing learned based inertial odometry and magnetometer measurements for obtaining lower drift position. The proposed algorithm does not need to calibrate the magnetometer bias, and effectively resist the influence of magnetic interference in the indoor environment, and can provide a very reliable absolute magnetic heading correction. Test results show that the proposed method can obtain better positioning performance than other methods using calibrated magnetometer observations.