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).
A UV SLAM Data Association JCBB
Jing Luo Bo He Peixun Wang Ke Yang Chunyun Ren
School of Information Science and Engineering, Ocean University of China Qingdao, 266100, P. R. China
国际会议
长沙
英文
886-889
2010-03-13（万方平台首次上网日期，不代表论文的发表时间）