Simultaneous localization and mapping implementation based on improved iterated unscented particle filter

LUO Yuan; XIONG Yan; ZHANG Yi

doi:10.3788/OPE.20152313.0560

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Simultaneous localization and mapping implementation based on improved iterated unscented particle filter

更新时间：2020-08-13

- Simultaneous localization and mapping implementation based on improved iterated unscented particle filter
- Optics and Precision Engineering Vol. 23, Issue 10z, Pages: 559-565(2015)
- 作者机构：
  
  重庆邮电大学信息无障碍工程研发中心重庆,400065
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152313.0560
  CLC： TP242.6
- Received：08 May 2015，
  
  Revised：19 May 2015，
  
  Published：14 November 2015
- 稿件说明：
移动端阅览
罗元, 熊艳, 张毅. 基于改进迭代无迹粒子滤波的同时定位与地图构建[J]. 光学精密工程, 2015,23(10z): 559-565

LUO Yuan, XIONG Yan, ZHANG Yi. Simultaneous localization and mapping implementation based on improved iterated unscented particle filter[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 559-565
罗元, 熊艳, 张毅. 基于改进迭代无迹粒子滤波的同时定位与地图构建[J]. 光学精密工程, 2015,23(10z): 559-565 DOI： 10.3788/OPE.20152313.0560.

LUO Yuan, XIONG Yan, ZHANG Yi. Simultaneous localization and mapping implementation based on improved iterated unscented particle filter[J]. Editorial Office of Optics and Precision Engineering, 2015,23(10z): 559-565 DOI： 10.3788/OPE.20152313.0560.

摘要

针对移动机器人同时定位与地图构建(SLAM)中的粒子滤波不一致问题

提出了新的改进算法。利用迭代无迹粒子滤波精确设计了粒子滤波器的提议分布

以迭代更新方式将当前激光传感器信息和里程计信息融入重要性采样过程

实现了改进迭代无迹粒子滤波算法

降低了滤波器预测阶段机器人位姿的不确定性并有效地减少了所需粒子的数量。使用配有URG激光传感器的Pioneer3-DX在机器人操作系统平台上与Fast SLAM算法进行了比较实验。结果表明

创建相同一致性地图时

改进算法仅使用10个粒子构建地图

平均消耗时间为325 s

除此之外还减少了所需粒子数量

提高了地图创建效率;同时机器人航向误差为-1.4861

降低了机器人位姿的不确定性。此外

对两种算法方差的比较可以看出改进算法的稳定度高于FastSLAM算法。

Abstract

For particle filter inconsistency in mobile robot Simultaneous Localization and Mapping(SLAM)

an improved unscented particle filter algorithm is proposed in this paper. To overcome this phenomenon mentioned above

the algorithm utilizes an iterated sigma point particle filter to generate more accurate proposal distribution

which fuses the robot's odometer information and laser information into sequential importance sampling routine through iterated update processing. The algorithm effectively improves the filter consistency and state estimation accuracy

and requires smaller number of particles. Based on the robot operating system

this algorithm is performed on a platform of Pioneer3-DX robot equipped with a URG laser range finder to compare with the traditional fast SLAM algorithm. Experimental results show that it creates a same consistency map and the improved algorithm with only 10 particles and consumes 325 s reduces the number of particles needed and improves the mapping efficiency. At the same time

the robot heading error is-1.4861

showing a lower uncertainty of the robot pose. In addition

it indicates that the stability of the improved algorithm is higher than that of the FastSLAM algorithm by comparing their variances.

关键词

Keywords

references

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张建伟,张立新,胡颖,等. 开源机器人操作系统-ROS[M]. 北京:科学出版社,2012. ZHANG J W, ZHANG L X, HU Y, et al. The Open Source Robot Operating System-ROS[M]. Beijing:Science Press,2012.(in Chinese)

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罗元,傅有力,程铁凤. 基于改进Rao-Blackwellized粒子滤波器的同时定位与地图构建[J]. 控制理论与应用,2015,32(2):267-272. LUO Y, FU Y L, CHENG T F. Simultaneous localization and mapping implementation based on the improved Rao-Blackwellized particle filter[J]. Control Theory & Applications, 2015, 32(2):267-272.(in Chinese)

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