LLIO: Lightweight Learned Inertial Odometer

Abstract

The 3-D 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 2-D trajectory estimation on the ground plane, which is not suitable for augmented reality (AR) applications. Tight learned inertial odometry (TLIO) proposed an inertial-based 3-D motion estimator that achieves very low position drift by using the raw inertial measurement unit (IMU) measurements and the displacement prediction 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 article, 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 a similar level of accuracy but remarkable efficiency improvement. Specifically, the proposed LLIO algorithm was implemented on mobile devices and compared the computational efficiency with TLIO. The inference efficiency of the proposed system is up to 12 times improved than that of TLIO. Source code can be found on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/i2Nav-WHU/LightweightLearnedInertialOdometergithub</uri> .