This paper presents a batch estimation method for Simultaneous Localization and Mapping (SLAM) using the Prediction Error Method (PEM). The estimation problem considers landmarks as parameter while treating dynamics using state space models. The gradient needed for parameter estimation is computed recursively using an Extended Kalman Filter (EKF). Results using simulations with a monocular camera and inertial sensors are presented and compared to a Nonlinear Least-Squares (NLS) estimator. The presented method produce both lower RMSE's and scale better to the batch length.
['Zoran Sjanic', 'Martin A. Skoglund']
Prediction error method estimation for Simultaneous Localisation and Mapping