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

ROBOMECH Journal

Kiyoshi Irie¹ 

[1] Future Robotics Technology Center, Chiba Institute of Technology, Narashino, Japan

关键词: Motion estimation;    Inertial measurement unit;    Graph-based simultaneous localization and mapping;   

DOI  :  10.1186/s40648-018-0110-1

学科分类：人工智能

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY