Online Self-Calibration for Visual-Inertial Navigation: Models, Analysis, and Degeneracy
Online Self-Calibration for Visual-Inertial Navigation: Models, Analysis, and Degeneracy
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DOI：
10.1109/tro.2023.3275878
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发表时间：
2023-10
影响因子：
7.8
通讯作者：
Yulin Yang;Patrick Geneva;Xingxing Zuo;G. Huang
中科院分区：
文献类型：
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作者：
Yulin Yang;Patrick Geneva;Xingxing Zuo;G. Huang
传感器标定在视觉-惯性传感器融合中起着重要的作用，本文对在线自标定进行了深入研究，以实现鲁棒、高精度的视觉-惯性状态估计。为此，我们首先进行完整的可观测性分析的视觉惯性导航系统（VINS）的传感参数，包括惯性测量单元（IMU）/相机的内在和IMU相机的时空外校准，沿着与读出时间的滚动快门（RS）相机（如果使用）的完整校准。我们研究包含内部参数的不同惯性模型变体，这些参数包含低成本惯性传感器最常用的模型。在此基础上，对线性化VINS进行了全传感器标定的可观测性分析。我们的分析从理论上证明了直觉通常假设在文献中，即，VINS与完整的传感器校准有四个不可观测的方向，对应于系统的全球偏航和位置，而所有的传感器校准参数是可观察的充分激发运动。此外，我们，第一次，确定退化的运动原语的IMU和相机的内在校准，当结合起来，可能会产生复杂的退化运动。我们比较了建议的在线自校准常用的伊穆斯对最先进的离线校准工具箱Kalibr，表明所提出的系统实现了更好的一致性和可重复性。基于我们的分析和实验评估，我们还提供了实用的指导方针，有效地执行在线IMU相机自校准在实践中。
As sensor calibration plays an important role in visual-inertial sensor fusion, this article performs an in-depth investigation of online self-calibration for robust and accurate visual-inertial state estimation. To this end, we first conduct complete observability analysis for visual-inertial navigation systems (VINS) with full calibration of sensing parameters, including inertial measurement unit (IMU)/camera intrinsics and IMU-camera spatial-temporal extrinsic calibration, along with readout time of rolling shutter (RS) cameras (if used). We study different inertial model variants containing intrinsic parameters that encompass most commonly used models for low-cost inertial sensors. With these models, the observability analysis of linearized VINS with full sensor calibration is performed. Our analysis theoretically proves the intuition commonly assumed in the literature—that is, VINS with full sensor calibration has four unobservable directions, corresponding to the system's global yaw and position, while all sensor calibration parameters are observable given fully excited motions. Moreover, we, for the first time, identify degenerate motion primitives for IMU and camera intrinsic calibration, which, when combined, may produce complex degenerate motions. We compare the proposed online self-calibration on commonly used IMUs against the state-of-art offline calibration toolbox Kalibr, showing that the proposed system achieves better consistency and repeatability. Based on our analysis and experimental evaluations, we also offer practical guidelines to effectively perform online IMU-camera self-calibration in practice.