论文标题
查看瞬间匹配的视觉惯性大满贯的鲁棒性
A Look at Improving Robustness in Visual-inertial SLAM by Moment Matching
论文作者
论文摘要
相机传感器和惯性数据的融合是自动和智能设备中自我运动跟踪的领先方法。依赖非线性过滤的状态估计技术是解决相关信息融合任务的强大范式。该空间中的事实上的推理方法是著名的扩展卡尔曼滤波器（EKF），它依赖于动力学和测量模型的一阶线性化。本文仔细研究了EKF所产生的实际含义和局限性，尤其是在有缺陷的视觉特征关联和存在强烈混杂的噪声下。作为替代方案，我们重新审视了贝叶斯过滤的假定密度公式，并采用了矩匹配（无知的卡尔曼过滤）方法来进行视觉持续频道和视觉大满贯。我们的结果突出了动态传播和视觉测量更新方面的鲁棒性重要方面，并且我们显示了Euroc MAV无人机数据基准的最新结果。
The fusion of camera sensor and inertial data is a leading method for ego-motion tracking in autonomous and smart devices. State estimation techniques that rely on non-linear filtering are a strong paradigm for solving the associated information fusion task. The de facto inference method in this space is the celebrated extended Kalman filter (EKF), which relies on first-order linearizations of both the dynamical and measurement model. This paper takes a critical look at the practical implications and limitations posed by the EKF, especially under faulty visual feature associations and the presence of strong confounding noise. As an alternative, we revisit the assumed density formulation of Bayesian filtering and employ a moment matching (unscented Kalman filtering) approach to both visual-inertial odometry and visual SLAM. Our results highlight important aspects in robustness both in dynamics propagation and visual measurement updates, and we show state-of-the-art results on EuRoC MAV drone data benchmark.
