论文标题
SE_N（3）组的闭合形式错误传播，用于不变的扩展Kalman过滤，并应用于VINS
Closed-form Error Propagation on the SE_n(3) Group for Invariant Extended Kalman Filtering with Applications to VINS
论文作者
论文摘要
姿势估计对于机器人感知，路径计划等很重要。机器人姿势可以在基质谎言组上建模，并且通常通过基于滤波器的方法估算。在本文中，我们在存在随机噪声的情况下建立了不变扩展卡尔曼滤波器（IEKF）的误差公式，并将其应用于视觉辅助惯性导航。我们通过OpenVins平台上的数值模拟和实验评估了算法。在Euroc公共MAV数据集上进行的仿真和实验都表明，我们的算法的表现优于某些基于最先进的过滤器的方法，例如基于Quaternion的EKF，首先估计Jacobian EKF等。
Pose estimation is important for robotic perception, path planning, etc. Robot poses can be modeled on matrix Lie groups and are usually estimated via filter-based methods. In this paper, we establish the closed-form formula for the error propagation for the Invariant extended Kalman filter (IEKF) in the presence of random noises and apply it to vision-aided inertial navigation. We evaluate our algorithm via numerical simulations and experiments on the OPENVINS platform. Both simulations and the experiments performed on the public EuRoC MAV datasets demonstrate that our algorithm outperforms some state-of-art filter-based methods such as the quaternion-based EKF, first estimates Jacobian EKF, etc.
