Data association in bearing-only SLAM using a cost function-based approach

When using an extended Kalman filter (EKF) in simultaneous localization and mapping (SLAM) for a mobile robot with bearing-only measurements, it is crucial to correctly assign correspondences between measurements and registered features in the map, otherwise the filter diverges or becomes inconsistent. Conventional methods based on the Mahalanobis distance metric may produce data association ambiguities. Its reliability may further be degraded in bearing-only SLAM due to the limited amount of information delivered from the sensor. The data association process is cast here as that of making a decision based on the sensor measurement as whether to update the EKF or not. For this, cost functions are applied taking into account the interferences from other features. The proposed approach enhances robustness of the data association and consequently assures the performance of bearing-only SLAM. Results from simulations and experiments are included to demonstrate the effectiveness of the method in a typical indoor senario.