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基于智能优化箱粒子滤波的移动机器人FastSLAM

罗景文 秦世引

罗景文, 秦世引. 基于智能优化箱粒子滤波的移动机器人FastSLAM[J]. 北京航空航天大学学报, 2022, 48(1): 53-66. doi: 10.13700/j.bh.1001-5965.2020.0549
引用本文: 罗景文, 秦世引. 基于智能优化箱粒子滤波的移动机器人FastSLAM[J]. 北京航空航天大学学报, 2022, 48(1): 53-66. doi: 10.13700/j.bh.1001-5965.2020.0549
LUO Jingwen, QIN Shiyin. FastSLAM for mobile robot based on box particle filter with intelligence optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(1): 53-66. doi: 10.13700/j.bh.1001-5965.2020.0549(in Chinese)
Citation: LUO Jingwen, QIN Shiyin. FastSLAM for mobile robot based on box particle filter with intelligence optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(1): 53-66. doi: 10.13700/j.bh.1001-5965.2020.0549(in Chinese)

基于智能优化箱粒子滤波的移动机器人FastSLAM

doi: 10.13700/j.bh.1001-5965.2020.0549
基金项目: 

国家自然科学基金 62063036

云南师范大学博士科研启动项目 01000205020503115

详细信息
    通讯作者:

    罗景文, E-mail:by1503117@buaa.edu.cn

  • 中图分类号: TP242.6;TB553

FastSLAM for mobile robot based on box particle filter with intelligence optimization

Funds: 

National Nature Science Foundation of China 62063036

Research Foundation for Doctor of Yunnan Normal University 01000205020503115

More Information
  • 摘要:

    针对传统FastSLAM算法需要大量粒子构建地图导致计算复杂度高、难以提高估计精度等问题,研究构建了一种基于智能优化箱粒子滤波(IOBPF)的移动机器人FastSLAM算法。首先,将萤火虫算法(FA)的动态寻优机制引入箱粒子滤波(BPF),建立了箱粒子的荧光亮度更新公式、吸引度计算公式和位置更新公式,使箱粒子集智能化地向高似然区域移动,避免了箱粒子的退化现象。然后,以改进的智能优化箱粒子滤波进行机器人位姿估计,并采用扩展区间卡尔曼滤波(EIKF)完成地图的构建和更新。移动机器人的模型仿真和实体实验结果表明:所提智能化FastSLAM算法可有效提升箱粒子的性能,并降低地图构建所需粒子数,从而显著提高FastSLAM的定位精度和地图构建的鲁棒性。

     

  • 图 1  二维空间中的箱粒子及其数学定义

    Figure 1.  Box particle and its mathematical definition in two-dimensional space

    图 2  IOBPF-SLAM算法的整体结构

    Figure 2.  Overall structure of IOBPF-SLAM algorithm

    图 3  模拟环境下的不同SLAM算法运行结果比较

    Figure 3.  Comparison of running results among different SLAM algorithms in simulation environment

    图 4  不同SLAM算法的RMSE比较

    Figure 4.  Comparison of RMSE among different SLAM algorithms

    图 5  不同SLAM算法的有效粒子数百分比

    Figure 5.  Percentage of effective particle number for different SLAM algorithms

    图 6  不同箱粒子的包含值

    Figure 6.  Inclusion values of different box particles

    图 7  不同SLAM算法的一致性比较

    Figure 7.  Comparison of consistency among different SLAM algorithms

    图 8  不同粒子数条件下算法的运行时间比较

    Figure 8.  Comparison of running time among algorithms under different particle numbers

    图 9  差分驱动轮式移动机器人及其实验场景

    Figure 9.  Differential drive wheeled mobile robot and experimental scene

    图 10  地图构建过程

    Figure 10.  Map building process

    图 11  IOBPF-SLAM的轨迹估计和路标估计结果

    Figure 11.  Estimation results of trajectory and landmarks by IOBPF-SLAM

    图 12  不同SLAM算法的路标位置估计误差比较

    Figure 12.  Comparison of estimation errors of landmark position among different SLAM algorithms

    图 13  IOBPF-SLAM生成的二维栅格地图

    Figure 13.  Two-dimensional grid map generated by IOBPF-SLAM

    表  1  二维箱粒子的基本数学定义

    Table  1.   Basic mathematical definition of two-dimensional box particle

    参数 数学表示
    宽度
    中心
    范数
    体积
    下载: 导出CSV

    表  2  仿真参数设置

    Table  2.   Simulation parameter setting

    参数 设定值
    机器人最大转向角 ±30°
    机器人最大转向角速度 ±2°/s
    机器人轴间距 2 m
    传感器的最大观测范围 20 m
    传感器的观测频率 50 Hz
    控制时间间隔 0.02 s
    控制噪声 (0.35 m/s, 2°)
    观测噪声 (0.3 m/s, 3°)
    下载: 导出CSV

    表  3  不同SLAM算法的性能比较

    Table  3.   Comparison of performance among different SLAM algorithms

    算法 粒子数 CPU时间/s 运行时间/s
    GridSLAM 100 0.873 318.72
    GMapping 30 0.401 261.37
    BPF-SLAM 15 0.325 223.41
    本文算法 10 0.337 247.69
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-09-25
  • 录用日期:  2020-11-13
  • 网络出版日期:  2022-01-20

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