LLIO: Lightweight Learned Inertial Odometer

The 3D position estimation of pedestrians is a vital module to build the connections between persons and things.

The traditional gait model-based methods cannot fulfill the various motion patterns.

And the various data-driven-based inertial odometry solutions focus on the 2D trajectory estimation on the ground plane, which is not suitable for AR applications.

TLIO (Tight Learned Inertial Odometry) proposed an inertial-based 3D motion estimator that achieves very low position drift by using the raw IMU measurements and the displacement predict coming from a neural network to provide low drift pedestrian dead reckoning.

However, TLIO is unsuitable for mobile devices because it is computationally expensive.

In this paper, a lightweight learned inertial odometry network (LLIO-Net) is designed for mobile devices.

By replacing the network in TLIO with the LLIO-Net, the proposed system shows similar accuracy but significantly improved efficiency.

Specifically, the proposed LLIO algorithm was implemented on mobile devices and compared the efficiency with TLIO.

The inference efficiency of the proposed system is 2-12 times that of TLIO.