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

Publication Type:
Conference Proceeding
Proceedings - IEEE International Conference on Robotics and Automation, 2007, pp. 4108 - 4113
Issue Date:
Filename Description Size
Thumbnail2006015152.pdf521.26 kB
Adobe PDF
Full metadata record
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 scenario. © 2007 IEEE.
Please use this identifier to cite or link to this item: