LGC-Net: A Lightweight Gyroscope Calibration Network for Efficient Attitude Estimation

TL;DR

LGC-Net is a lightweight neural network designed for real-time gyroscope calibration and attitude estimation, effectively denoising low-cost MEMS gyroscopes by extracting local and global features, achieving state-of-the-art results without using vision sensors.

Contribution

The paper introduces a novel lightweight neural network architecture that combines depthwise separable convolution and large kernel attention for efficient gyroscope calibration and attitude estimation.

Findings

Achieves state-of-the-art performance on EuRoC and TUM-VI datasets.

Provides comparable orientation accuracy to visual-inertial odometry systems.

Offers a lightweight model suitable for real-time applications.

Abstract

This paper presents a lightweight, efficient calibration neural network model for denoising low-cost microelectromechanical system (MEMS) gyroscope and estimating the attitude of a robot in real-time. The key idea is extracting local and global features from the time window of inertial measurement units (IMU) measurements to regress the output compensation components for the gyroscope dynamically. Following a carefully deduced mathematical calibration model, LGC-Net leverages the depthwise separable convolution to capture the sectional features and reduce the network model parameters. The Large kernel attention is designed to learn the long-range dependencies and feature representation better. The proposed algorithm is evaluated in the EuRoC and TUM-VI datasets and achieves state-of-the-art on the (unseen) test sequences with a more lightweight model structure. The estimated orientation with our LGC-Net is comparable with the top-ranked visual-inertial odometry systems, although it does not adopt vision sensors. We make our method open-source at: https://github.com/huazai665/LGC-Net