Calib-Net: Calibrating the Low-Cost IMU via Deep Convolutional Neural Network

Frontiers in Robotics and AI

Ruihao Li ¹  Wei Yi ¹  Xiaodong Yi ¹  Chunlian Fu² 

[1] Artificial Intelligence Research Center (AIRC), Defense Innovation Institute, Beijing, China;Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, China;

关键词: IMU calibration;    deep neural network;    orientation estimation;    spatio-temporal;    visual inertial odometry;   

DOI  :  10.3389/frobt.2021.772583

来源: DOAJ

【 摘 要 】

The low-cost Inertial Measurement Unit (IMU) can provide orientation information and is widely used in our daily life. However, IMUs with bad calibration will provide inaccurate angular velocity and lead to rapid drift of integral orientation in a short time. In this paper, we present the Calib-Net which can achieve the accurate calibration of low-cost IMU via a simple deep convolutional neural network. Following a carefully designed mathematical calibration model, Calib-Net can output compensation components for gyroscope measurements dynamically. Dilation convolution is adopted in Calib-Net for spatio-temporal feature extraction of IMU measurements. We evaluate our proposed system on public datasets quantitively and qualitatively. The experimental results demonstrate that our Calib-Net achieves better calibration performance than other methods, what is more, and the estimated orientation with our Calib-Net is even comparable with the results from visual inertial odometry (VIO) systems.

【 授权许可】

Unknown