A Plug-and-Play Learning-based IMU Bias Factor for Robust Visual-Inertial Odometry

• URL: http://arxiv.org/abs/2503.12527v2
• Date: Fri, 17 Oct 2025 03:20:42 GMT
• Title: A Plug-and-Play Learning-based IMU Bias Factor for Robust Visual-Inertial Odometry
• Authors: Yang Yi, Kunqing Wang, Jinpu Zhang, Zhen Tan, Xiangke Wang, Hui Shen, Dewen Hu,
• Abstract summary: We propose a novel plug-and-play module featuring the Inertial Prior Network (IPNet)<n>IPNet infers an IMU bias prior by implicitly capturing the motion characteristics of specific platforms.<n>In this work, we first directly infer the biases prior only using the raw IMU data using a sliding window approach.
• Score: 27.62788405443008
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Accurate and reliable estimation of biases of low-cost Inertial Measurement Units (IMU) is a key factor to maintain the resilience of Visual-Inertial Odometry (VIO), particularly when visual tracking fails in challenging areas. In such cases, bias estimates from the VIO can deviate significantly from the real values because of the insufficient or erroneous vision features, compromising both localization accuracy and system stability. To address this challenge, we propose a novel plug-and-play module featuring the Inertial Prior Network (IPNet), which infers an IMU bias prior by implicitly capturing the motion characteristics of specific platforms. The core idea is inspired intuitively by the observation that different platforms exhibit distinctive motion patterns, while the integration of low-cost IMU measurements suffers from unbounded error that quickly accumulates over time. Therefore, these specific motion patterns can be exploited to infer the underlying IMU bias. In this work, we first directly infer the biases prior only using the raw IMU data using a sliding window approach, eliminating the dependency on recursive bias estimation combining visual features, thus effectively preventing error propagation in challenging areas. Moreover, to compensate for the lack of ground-truth bias in most visual-inertial datasets, we further introduce an iterative method to compute the mean per-sequence IMU bias for network training and release it to benefit society. The framework is trained and evaluated separately on two public datasets and a self-collected dataset. Extensive experiments show that our method significantly improves localization precision and robustness.

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