A Robust Data Association for Simultaneous Localization and Mapping in Dynamic Environments
JPDA (Joint Probabilistic Data Association) is vastly regarded as a more tractable and suboptimal method for solving ambiguity in data association problem in the presence of clutter for simultaneous localization and mapping (SLAM). However, JPDA generally has problems for detecting moving objects and distinguishing the new landmarks from clutter, which cause false data associations in dynamic environments. We propose a semi-temporal algorithm using three-scan JPDA to accurately correlate the observation with its corresponding landmark, and initialize the new landmark. The existence of moving clutter in validation gates is alerted by a statistic motion detector that enhances data association in a dynamic environment. This method can be applied for real-time SLAM applications with less complexity comparing with other high-cost optimal Bayesian filter. Simulation is performed to verify the effectiveness of method.
Joint Probability Data Association (JPDA) Normalized Innovation Squared (NIS)
Rex H.Wong Jizhong Xiao Samleo L.Joseph
Department of Electrical Engineering The City College of the City University of New York New York,NY 10031,USA
国际会议
2010 IEEE信息与自动化国际会议(ICIA 2010)
哈尔滨
英文
1-6
2010-06-20（万方平台首次上网日期，不代表论文的发表时间）