A Plug-and-Play Learning-based IMU Bias Factor for Robust Visual-Inertial Odometry
Journal:
arXiv
Published Date:
Mar 16, 2025
Abstract
The bias of low-cost Inertial Measurement Units (IMU) is a critical factor
affecting the performance of Visual-Inertial Odometry (VIO). In particular,
when visual tracking encounters errors, the optimized bias results may deviate
significantly from the true values, adversely impacting the system's stability
and localization precision. In this paper, we propose a novel plug-and-play
framework featuring the Inertial Prior Network (IPNet), which is designed to
accurately estimate IMU bias. Recognizing the substantial impact of initial
bias errors in low-cost inertial devices on system performance, our network
directly leverages raw IMU data to estimate the mean bias, eliminating the
dependency on historical estimates in traditional recursive predictions and
effectively preventing error propagation. Furthermore, we introduce an
iterative approach to calculate the mean value of the bias for network
training, addressing the lack of bias labels in many visual-inertial datasets.
The framework is evaluated on two public datasets and one self-collected
dataset. Extensive experiments demonstrate that our method significantly
enhances both localization precision and robustness, with the ATE-RMSE metric
improving on average by 46\%. The source code and video will be available at
\textcolor{red}{https://github.com/yiyscut/VIO-IPNet.git}.
