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中南大学学报(自然科学版)

Journal of Central South University

第43卷    第2期    总第210期    2012年2月

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文章编号:1672-7207(2012)02-0522-06
一种移动机器人SLAM中的多假设数据关联方法
陈白帆,蔡自兴,邹智荣

(中南大学 信息科学与工程学院, 湖南 长沙,410083)

摘 要: 针对移动机器人同时定位与建图(SLAM)中的局部数据关联问题,提出一种基于粒子滤波的多假设数据关联方法。该方法将数据关联问题转换成离散优化问题,利用多个粒子来维持多种数据关联假设,通过计算关联代价来获得粒子权重,用基本剪枝技术在粒子重采样过程中滤除错误的数据关联假设。研究结果表明:该方法弥补了经典的数据关联方法中关联假设一旦确定就不能修改的不足;与ICNN和JCBB数据关联方法相比,该方法能获得更正确的数据关联结果和更高的定位精度。

 

关键字: 移动机器人;同时定位与建图;数据关联;多假设

A multiple hypotheses data association method in mobile robot SLAM
CHEN Bai-fan, CAI Zi-xing, ZOU Zhi-rong

School of Information Science and Engineering, Central South University, Changsha 410083, China

Abstract:According to the local data association problem in mobile robot SLAM process, a new multiple hypotheses data association method based on the particle filter was presented. In the method, the data association problem was transformed as the discrete optimization, and multiple particles were used to maintain the multiple data association hypotheses and every particle’s weight was calculated by association cost. During the resample, the wrong hypotheses were discarded through basic branch and bound approach. The results show that the method resolves the problem where the classic method cannot modify the previous association hypothesis. By experimental results analysis and comparison, the new method ca, n reach more correct data association results and higher location precision than the classic ICNN and JCBB method.

 

Key words: mobile robot; simultaneous localization and mapping; data association; multiple hypotheses

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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