Dynamic Attitude Estimation Improvement for Low-cost MEMS IMU by Integrating Low-cost GPS

TL;DR

This paper introduces a low-cost 6-DOF navigation system for small aerial robots that integrates GPS and MEMS sensors, with a novel fusion algorithm that estimates motion states and synchronization errors simultaneously, achieving high update rates.

Contribution

It presents a new fusion algorithm that improves attitude estimation accuracy by estimating time synchronization errors in low-cost GPS/IMU systems, suitable for embedded implementation.

Findings

Achieves over 100 Hz update rate on embedded microprocessor

Demonstrates improved attitude accuracy in moving aerial robots

Reduces computational load compared to existing methods

Abstract

This paper proposes a low-cost six Degree-of-Freedom (6-DOF) navigation system for small aerial robots based on the integration of Global Position System (GPS) receiver with sensors of inertional Microelectromechanical Systems (MEMS). In the problem of fusing Inertial Measurement Unit (IMU) with low-cost GPS, the effect of time synchronization error on attitude estimation is concerned. A fusion algorithm which can estimate the motion states and the time synchronization error simultaneously is proposed. This algorithm adds a time estimation loop to improve estimation accuracy. Compared with another states augmented estimation approach, this method has the advantages of lower computation burden, avoidance of the discretization error in the low sample rate. The estimation algorithm is implemented in an low-cost embedded microprocessor where the update rate of algorithm can achieve more than 100 Hz, and therefore high-performance computational units are not necessary. In robotic experiment, the proposed algorithm serves as the navigation solution for a small aerial robot. The accuracy and reliability of the self-designed system are tested when the robot is moving with significant acceleration.