一种自适应的GraphSLAM鲁棒闭环算法

张国良, 姚二亮, 汤文俊, 岳亚南

张国良, 姚二亮, 汤文俊, 岳亚南. 一种自适应的GraphSLAM鲁棒闭环算法[J]. 信息与控制, 2015, 44(3): 316-320,327. DOI: 10.13976/j.cnki.xk.2015.0316
引用本文: 张国良, 姚二亮, 汤文俊, 岳亚南. 一种自适应的GraphSLAM鲁棒闭环算法[J]. 信息与控制, 2015, 44(3): 316-320,327. DOI: 10.13976/j.cnki.xk.2015.0316
ZHANG Guoliang, YAO Erliang, TANG Wenjun, YUE Yanan. An Adaptive Robust Loop Closure Algorithm for Graph SLAM[J]. INFORMATION AND CONTROL, 2015, 44(3): 316-320,327. DOI: 10.13976/j.cnki.xk.2015.0316
Citation: ZHANG Guoliang, YAO Erliang, TANG Wenjun, YUE Yanan. An Adaptive Robust Loop Closure Algorithm for Graph SLAM[J]. INFORMATION AND CONTROL, 2015, 44(3): 316-320,327. DOI: 10.13976/j.cnki.xk.2015.0316
张国良, 姚二亮, 汤文俊, 岳亚南. 一种自适应的GraphSLAM鲁棒闭环算法[J]. 信息与控制, 2015, 44(3): 316-320,327. CSTR: 32166.14.xk.2015.0316
引用本文: 张国良, 姚二亮, 汤文俊, 岳亚南. 一种自适应的GraphSLAM鲁棒闭环算法[J]. 信息与控制, 2015, 44(3): 316-320,327. CSTR: 32166.14.xk.2015.0316
ZHANG Guoliang, YAO Erliang, TANG Wenjun, YUE Yanan. An Adaptive Robust Loop Closure Algorithm for Graph SLAM[J]. INFORMATION AND CONTROL, 2015, 44(3): 316-320,327. CSTR: 32166.14.xk.2015.0316
Citation: ZHANG Guoliang, YAO Erliang, TANG Wenjun, YUE Yanan. An Adaptive Robust Loop Closure Algorithm for Graph SLAM[J]. INFORMATION AND CONTROL, 2015, 44(3): 316-320,327. CSTR: 32166.14.xk.2015.0316

一种自适应的GraphSLAM鲁棒闭环算法

详细信息
    作者简介:

    张国良(1970-),男,博士,教授.研究领域为智能机器人,先进控制理论与应用,组合导航.
    姚二亮(1991-),男,硕士生.研究领域为移动机器人SLAM.
    汤文俊(1986-),男,博士生.研究领域为多机器人SLAM.

    通讯作者:

    汤文俊,familyyao915@gmail.com

  • 中图分类号: TP242

An Adaptive Robust Loop Closure Algorithm for Graph SLAM

  • 摘要: 针对在常规Graph SLAM(simultaneous location and mapping)算法中后端优化无法高效排除错误闭环影响的问题,提出一种自适应的Graph SLAM鲁棒闭环算法.通过分析代价函数中尚未确定的参数对优化过程的影响,根据迭代得到的最新信息,对这些参数进行更新,从而加快算法收敛速度,并对不同的数据集有很好的适应性.利用公开的数据集对算法进行实验,通过对比发现,在添加不同类型、不同数量的异常闭环条件下,本文算法对不同数据集具有良好适应性且收敛速度较快,证明了算法的有效性.
    Abstract: We propose an adaptive robust loop closure algorithm for the Graph SLAM to address the problem where the back-end for conventional Graph SLAM obviates the influence of false loops efficiently. The influence of indefinite parameters in the cost function to the optimization procedure is analyzed. The parameters are renewed by the latest information obtained from iterations to speed the convergence rate. The algorithm is adaptive to different datasets. The experiment is performed for the proposed algorithm with public datasets. The comparison results show that the proposed algorithm is adaptive to different datasets with different types and numbers of outliers and the convergence rate is higher, which verifies the efficiency of the algorithm.
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出版历程
  • 收稿日期:  2014-05-27
  • 发布日期:  2015-06-19

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