Data Association for AUV Localization and Map Building

Data association is one of the most difficult problems in Simultaneous Localization and Mapping (SLAM). As for Autonomous Underwater Vehicle (AUV), reliable data association is particularly important because of complex and mutable underwater environment. In this paper two prevailing data association algorithms-Individual Compatibility Nearest Neighbor (ICNN) and Joint Compatibility Branch and Bound (JCBB) are compared by simulation experiments and then some improvements on the computational complexity of JCBB are presented in order to seek a robust data association method for real-time application of our AUV. The SLAM algorithm used in the experiments is based on Extended Kalman Filter (EKF).