A graph optimization approach for motion estimation using inertial measurement unit data

This study presents a novel approach for processing motion data from a six-degree-of-freedom inertial measurement unit (IMU). Trajectory estimation through double integration of acceleration measurements results in the generation and accumulation of multiple errors. Existing IMU-based measurement methods often use constrained initial and final states to resolve these errors. The constraints on the initial and final states lead to a uniform distribution of the accumulated errors throughout the calculated trajectory so that they cancel each other. We develop a generalized method that can incorporate the constraints from the measurements of intermediate states. The proposed approach is inspired by graph-based simultaneous localization and mapping processes from robotics research. We tested the proposed method with simulated and actual IMU data and found that our method estimates trajectories more accurately than conventional methods with acceptably higher computational costs. © 2018, The Author(s).