论文标题
视觉惯性导航系统的在线自我校准:模型,分析和退化
Online Self-Calibration for Visual-Inertial Navigation Systems: Models, Analysis and Degeneracy
论文作者
论文摘要
在本文中,我们深入研究了在线自我校准的问题,以实现稳健和准确的视觉惯用状态估计。特别是,我们首先对视觉惯性导航系统(VIN)进行完整的可观察性分析,并具有全面的感应参数校准,包括IMU和摄像机内在和IMU相机时空外部校准,以及滚动快门(RS)摄像机的读取时间(如果使用)。我们研究了包含IMU内在参数的不同惯性模型变体,这些变体涵盖了低成本惯性传感器的最常用模型。可观察性分析结果证明,具有完整传感器校准的VIN具有四个不可观察的方向,与系统的全局偏航和翻译相对应,而所有传感器校准参数均可观察到完全激发的6轴运动。此外,我们首次确定了IMU和摄像机内在校准的原始退化运动。每个退化的动作曲线将导致一组校准参数无法观察,并且这些退化运动的任何组合仍然是退化的。进行广泛的蒙特卡罗模拟和现实世界实验,以验证可观察性分析和确定的退化运动,表明在线自我校准提高了系统的准确性和鲁棒性,对校准不准确。我们将普遍使用的IMU的在线自我校准与最新的离线校准工具箱Kalibr进行了比较,并表明所提出的系统实现了更好的一致性和可重复性。根据我们的分析和实验评估,我们还提供了有关如何执行在线IMU-CAMERA摄像机自校准的实用准则。
In this paper, we study in-depth the problem of online self-calibration for robust and accurate visual-inertial state estimation. In particular, we first perform a complete observability analysis for visual-inertial navigation systems (VINS) with full calibration of sensing parameters, including IMU and camera intrinsics and IMU-camera spatial-temporal extrinsic calibration, along with readout time of rolling shutter (RS) cameras (if used). We investigate different inertial model variants containing IMU intrinsic parameters that encompass most commonly used models for low-cost inertial sensors. The observability analysis results prove that VINS with full sensor calibration has four unobservable directions, corresponding to the system's global yaw and translation, while all sensor calibration parameters are observable given fully-excited 6-axis motion. Moreover, we, for the first time, identify primitive degenerate motions for IMU and camera intrinsic calibration. Each degenerate motion profile will cause a set of calibration parameters to be unobservable and any combination of these degenerate motions are still degenerate. Extensive Monte-Carlo simulations and real-world experiments are performed to validate both the observability analysis and identified degenerate motions, showing that online self-calibration improves system accuracy and robustness to calibration inaccuracies. We compare the proposed online self-calibration on commonly-used IMUs against the state-of-art offline calibration toolbox Kalibr, and show that the proposed system achieves better consistency and repeatability. Based on our analysis and experimental evaluations, we also provide practical guidelines for how to perform online IMU-camera sensor self-calibration.