论文标题
TLIO：紧密学习的惯性进程仪
TLIO: Tight Learned Inertial Odometry
论文作者
论文摘要
在这项工作中，我们提出了一个紧密耦合的扩展Kalman滤波器框架，以进行仅IMU状态估计。皮带向下的IMU测量值基于IMU运动学运动模型提供了相对状态估计。但是，测量的整合对传感器偏置和噪声敏感，在几秒钟内导致显着漂移。 Yan等人的最新研究。 （Ronin）和Chen等。 （Ionet）显示了使用经过训练的神经网络从IMU数据段获得准确的2D位移估计值的能力，并通过使它们串联获得了良好的位置估计。本文展示了一个回归3D位移估计值及其不确定性的网络，使我们能够将相对状态测量紧密融合到随机克隆的EKF中，以求解姿势，速度和传感器偏见。我们表明，我们的网络接受了来自耳机的行人数据训练，可以产生统计上一致的测量和不确定性，以用作过滤器中的更新步骤，并且紧密耦合的系统在位置估计中优于速度整合方法，而AHRS态度态度过滤在方向估计中。
In this work we propose a tightly-coupled Extended Kalman Filter framework for IMU-only state estimation. Strap-down IMU measurements provide relative state estimates based on IMU kinematic motion model. However the integration of measurements is sensitive to sensor bias and noise, causing significant drift within seconds. Recent research by Yan et al. (RoNIN) and Chen et al. (IONet) showed the capability of using trained neural networks to obtain accurate 2D displacement estimates from segments of IMU data and obtained good position estimates from concatenating them. This paper demonstrates a network that regresses 3D displacement estimates and its uncertainty, giving us the ability to tightly fuse the relative state measurement into a stochastic cloning EKF to solve for pose, velocity and sensor biases. We show that our network, trained with pedestrian data from a headset, can produce statistically consistent measurement and uncertainty to be used as the update step in the filter, and the tightly-coupled system outperforms velocity integration approaches in position estimates, and AHRS attitude filter in orientation estimates.
