An Ensemble Kalman Filter for Feature-Based SLAM with Unknown Associations

In this paper, we present a new approach for solving the SLAM problem using the Ensemble Kalman Filter (EnKF). In contrast to other Kalman filter based approaches, the EnKF uses a small set of ensemble members to represent the state, thus circumventing the computation of the large covariance matrix traditionally used with Kalman filters, making this approach a viable application in high-dimensional state spaces. Our approach adapts techniques from the geoscientific community such as localization to the SLAM problem domain as well as using the Optimal Subpattern Assignment (OSPA) metric for data association. We then compare the results of our algorithm with an extended Kalman filter (EKF) and FastSLAM, showing that our approach yields a more robust, accurate, and computationally less demanding solution than the EKF and similar results to FastSLAM.