Simultaneous localization and mapping (SLAM) is a method that constructs a map of an unknown environment and localizes the position of a moving agent on the map simultaneously. Extended Kalman filter (EKF) has been widely adopted as a low complexity solution for online SLAM, which relies on a motion and measurement model of the moving agent. In practice, however, acquiring precise information about these models is very challenging, and the model mismatch effect causes severe performance loss in SLAM. In this paper, inspired by the recently proposed KalmanNet, we present a robust EKF algorithm using the power of deep learning for online SLAM, referred to as Split-KalmanNet. The key idea of Split-KalmanNet is to compute the Kalman gain using the Jacobian matrix of a measurement function and two recurrent neural networks (RNNs). The two RNNs independently learn the covariance matrices for a prior state estimate and the innovation from data. The proposed split structure in the computation of the Kalman gain allows to compensate for state and measurement model mismatch effects independently. Numerical simulation results verify that Split-KalmanNet outperforms the traditional EKF and the state-of-the-art KalmanNet algorithm in various model mismatch scenarios.
翻译:扩展 Kalman 过滤器( EKF) 已被广泛作为在线 SLAM 的低复杂解决方案, 依赖于移动代理器的运动和测量模型。 然而,在实践中, 获取有关这些模型的准确信息非常具有挑战性, 模型不匹配效应导致SLAM 严重性效失。 本文中, 受最近提议的 KalmanNet 的启发, 我们展示了一种强大的 EKF 算法, 使用了在线 SLAM 深度学习的力量, 称为 Split- Kalman Net 。 Slip- Kalman Net 的关键理念是使用 Jacobian 的测量函数矩阵和两个经常性神经网络( RNNS ) 来计算 Kalman 的收益。 两个 RNNS 独立学习了先前国家估计和数据创新的共变量矩阵。 在计算Kalman 收益时, 拟议的分裂结构可以独立地补偿状态和测量模型不匹配效应。 Numerficalimeal- Kalman Net 的模型模拟结果验证了Splain- Kal- Kalman 和 Kalperestal- Fashimactsimactal- fasimactal- asimacts asmal- sidealformationsidealmastrations asmactalpsmational- gislations.