基于因子图的鲁棒视觉惯性里程计的深度IMU偏置推断
Deep IMU Bias Inference for Robust Visual-Inertial Odometry with Factor Graphs
AI总结：
针对视觉失效下VIO易发散的问题，提出用LSTM和Transformer学习IMU偏置演化的方法，可泛化到未见过的运动模式，平均降低15%漂移率，且跨运动模式无需重训练。
摘要：
视觉惯性里程计（VIO）是移动平台最成熟的状态估计方法之一。但当视觉跟踪失效时，VIO算法会因惯性数据积分过程中的误差快速累积而迅速发散。该误差通常被建模为加性高斯噪声与以随机游走方式演化的缓慢变化偏置的组合。在本研究中，我们提出训练一个神经网络来学习真实的偏置演化过程。我们实现并对比了两种常见的序列深度学习架构：LSTM与Transformer。我们的方法源于近期基于学习的惯性估计器，但并非学习运动模型，而是明确针对IMU偏置，这使我们能够泛化到训练中未见过的运动模式。我们表明，所提方法在四足机器人、行走人类与无人机的各类运动场景下，提升了视觉挑战性环境中的状态估计效果。实验显示，漂移率平均降低15%，在完全视觉失效的情况下降幅更大。重要的是，我们还证明用一种运动模式（人类行走）训练的模型，无需重新训练即可应用于另一种模式（四足机器人小跑）。
英文摘要：
Visual Inertial Odometry (VIO) is one of the most established state estimation methods for mobile platforms. However, when visual tracking fails, VIO algorithms quickly diverge due to rapid error accumulation during inertial data integration. This error is typically modeled as a combination of additive Gaussian noise and a slowly changing bias which evolves as a random walk. In this work, we propose to train a neural network to learn the true bias evolution. We implement and compare two common sequential deep learning architectures: LSTMs and Transformers. Our approach follows from recent learning-based inertial estimators, but, instead of learning a motion model, we target IMU bias explicitly, which allows us to generalize to locomotion patterns unseen in training. We show that our proposed method improves state estimation in visually challenging situations across a wide range of motions by quadrupedal robots, walking humans, and drones. Our experiments show an average 15% reduction in drift rate, with much larger reductions when there is total vision failure. Importantly, we also demonstrate that models trained with one locomotion pattern (human walking) can be applied to another (quadruped robot trotting) without retraining.
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