移动机器人SLAM的混合数据关联方法

Baifan Chen, Z. Cai, Zhirong Zou
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引用次数: 8

摘要

数据关联是移动机器人同步定位与绘图的关键。经典的数据关联算法各有优缺点,如个体兼容最近邻(ICNN)算法和联合兼容分支定界(JCBB)算法。本文提出了一种基于局部地图的混合数据关联方法。首先利用ICNN在局部地图中进行数据关联,局部地图的排列由预先设定的阈值决定。为了克服ICNN的低可靠性问题,需要对数据关联结果进行错误检测。如果存在错配,JCBB将在错配测量附近的局部区域进行校正,以提高正确率。实验结果表明,即使在复杂环境下,该方法也具有令人满意的速度和精度。
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 approach for mobile robot SLAM
Data association is critical for the simultaneous localization and mapping (SLAM) of mobile robots. The classic data association algorithms have their own advantages and disadvantages, such as individual compatibility nearest neighbor (ICNN) algorithm and joint compatibility branch and bound (JCBB) algorithm. In this paper, we present a hybrid approach of data association based on local maps by combining them. ICNN is firstly used to do data association in the local map whose arrange is determined by the preset threshold. In order to overcome the problem of low reliability of ICNN, the errors detection in the data association results is necessary. If there are mismatchings, JCBB will be used to correct them in the local area around mismatched measurements to enhance the correct rate. The experimental results show that the proposed method performance of the speed and accuracy is satisfactory, even in the complex environments.
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