SLAM中的混合数据关联策略

J. Xiong, Yan Li
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引用次数: 3

摘要

数据关联是SLAM中的关键问题之一。针对神经网络数据关联精度低但效率高和JCBB数据关联精度高但效率低的问题,提出了一种混合数据关联策略。首先NN产生一个假设H,根据条件来决定我们是否纠正它或者采取什么样的纠正动作来得到新的假设。仿真结果表明,无论是静态环境还是动态环境,该方法都是有效的,相关结果可靠,时间性能明显优于JCBB。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Hybrid Data Association Strategy in SLAM
Data association is one of the key issues in SLAM. Against the accuracy of NN data association is low, but its efficiency is high and JCBB data association have high accuracy, but its efficiency is low, a hybrid data association strategy is proposed. At first NN produce an assumption H, according to conditions to decide whether we correct it or take what kind of corrective actions to get new assumption. Simulation results show that, both static environment and dynamic environment, this method is effective, the correlation results are reliable and its time performance is significantly better than JCBB.
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