  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.
