EDI：基于ESKF的视觉-惯性SLAM系统分离式初始化
EDI: ESKF-based Disjoint Initialization for Visual-Inertial SLAM Systems
AI总结：
本文提出EDI，一种基于误差状态卡尔曼滤波器的分离式视觉-惯性初始化方法，通过估计陀螺仪偏置、校正旋转并引入加权最小二乘闭式解，在EuRoC数据集上3秒内实现5.8%的平均尺度误差，优于现有方法。
摘要：
视觉-惯性初始化可分为联合方法和分离方法。联合方法同时处理视觉参数和惯性参数，通过基于IMU积分的特征点观测对齐，然后利用视觉和加速度观测的闭式解来求解初始速度和重力。相比之下，分离方法独立求解运动恢复结构（SFM）问题，并根据纯单目SLAM获得的尺度未知相机位姿确定惯性参数。然而，以往的分离方法存在局限性，例如假设加速度偏置影响可忽略，或依赖纯单目SLAM实现精确的旋转估计。为解决这些问题，我们提出EDI，一种用于快速、精确且鲁棒的视觉-惯性初始化的新方法。我们的方法引入误差状态卡尔曼滤波器（ESKF）来估计陀螺仪偏置并校正来自单目SLAM的旋转估计，从而克服了对纯单目SLAM旋转估计的依赖。为了在无先验信息的情况下估计尺度因子，我们提供了一种用于初始速度、尺度、重力和加速度偏置估计的闭式解。为解决重力与加速度偏置的耦合问题，我们在线性最小二乘方程中引入权重，确保加速度偏置的可观测性并处理离群值。在EuRoC数据集上的广泛评估表明，我们的方法在不到3秒内实现了5.8%的平均尺度误差，优于其他最先进的分离式视觉-惯性初始化方法，即使在具有挑战性的环境和人工噪声干扰下也是如此。
英文摘要：
Visual-inertial initialization can be classified into joint and disjoint approaches. Joint approaches tackle both the visual and the inertial parameters together by aligning observations from feature-bearing points based on IMU integration then use a closed-form solution with visual and acceleration observations to find initial velocity and gravity. In contrast, disjoint approaches independently solve the Structure from Motion (SFM) problem and determine inertial parameters from up-to-scale camera poses obtained from pure monocular SLAM. However, previous disjoint methods have limitations, like assuming negligible acceleration bias impact or accurate rotation estimation by pure monocular SLAM. To address these issues, we propose EDI, a novel approach for fast, accurate, and robust visual-inertial initialization. Our method incorporates an Error-state Kalman Filter (ESKF) to estimate gyroscope bias and correct rotation estimates from monocular SLAM, overcoming dependence on pure monocular SLAM for rotation estimation. To estimate the scale factor without prior information, we offer a closed-form solution for initial velocity, scale, gravity, and acceleration bias estimation. To address gravity and acceleration bias coupling, we introduce weights in the linear least-squares equations, ensuring acceleration bias observability and handling outliers. Extensive evaluation on the EuRoC dataset shows that our method achieves an average scale error of 5.8% in less than 3 seconds, outperforming other state-of-the-art disjoint visual-inertial initialization approaches, even in challenging environments and with artificial noise corruption.
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