在线学习视觉惯性里程计的测量不确定性，提升定位精度与鲁棒性。
Statistical Uncertainty Learning for Robust Visual-Inertial State Estimation
- 基于多视角几何一致性自监督，实时学习传感器测量可靠性。
- 在EuRoC数据集上，平移误差降低24%，旋转误差降低42%。
- 适合需要高精度定位的自动驾驶与机器人应用。
鲁棒视觉惯性里程计（VIO）的一个核心挑战是动态评估传感器测量的可靠性，这对合理加权各测量对状态估计的贡献至关重要。传统方法通常假设所有测量具有静态、统一的不确定性，这一简化难以捕捉真实数据中的动态误差特性。为此，本文提出一种统计框架，可直接从传感器数据和优化结果中在线学习测量可靠性。该方法利用多视图几何一致性作为自监督信号，实现特征点不确定性推断，并在优化中自适应加权视觉测量。我们在公开的EuRoC数据集上验证了该方法，相比使用固定不确定性参数的基线方法，平均翻译误差降低约24%，旋转误差降低42%。所提框架运行于实时系统，兼具更高精度与更强鲁棒性。为促进复现与进一步研究，代码将公开发布。
原文摘要 · Abstract (English)
A fundamental challenge in robust visual-inertial odometry (VIO) is to dynamically assess the reliability of sensor measurements. This assessment is crucial for properly weighting the contribution of each measurement to the state estimate. Conventional methods often simplify this by assuming a static, uniform uncertainty for all measurements. This heuristic, however, may be limited in its ability to capture the dynamic error characteristics inherent in real-world data. To improve this limitation, we present a statistical framework that learns measurement reliability assessment online, directly from sensor data and optimization results. Our approach leverages multi-view geometric consistency as a form of self-supervision. This enables the system to infer landmark uncertainty and adaptively weight visual measurements during optimization. We evaluated our method on the public EuRoC dataset, demonstrating improvements in tracking accuracy with average reductions of approximately 24\% in translation error and 42\% in rotation error compared to baseline methods with fixed uncertainty parameters. The resulting framework operates in real time while showing enhanced accuracy and robustness. To facilitate reproducibility and encourage further research, the source code will be made publicly available.
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