[1]罗元,余佳航,汪龙峰,等.改进RBPF的移动机器人同步定位与地图构建[J].智能系统学报,2015,10(03):460-464.[doi:10.3969/j.issn.1673-4785.201404024]
 LUO Yuan,YU Jiahang,WANG Longfeng,et al.Simultaneous localization and mapping of an improved RBPF based mobile robot[J].CAAI Transactions on Intelligent Systems,2015,10(03):460-464.[doi:10.3969/j.issn.1673-4785.201404024]
点击复制

改进RBPF的移动机器人同步定位与地图构建(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年03期
页码:
460-464
栏目:
学术论文—智能系统
出版日期:
2015-06-25

文章信息/Info

Title:
Simultaneous localization and mapping of an improved RBPF based mobile robot
作者:
罗元 余佳航 汪龙峰 王运凯
重庆邮电大学 国家信息无障碍工程研发中心, 重庆 400065
Author(s):
LUO Yuan YU Jiahang WANG Longfeng WANG Yunkai
National Information Accessibility Engineering Research & Development Center, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
关键词:
移动机器人Rao-Blackwellized粒子滤波器同步定位与地图构建(SLAM)Gmapping算法自适应重采样技术
Keywords:
mobile robotRao-Blackwellized particle filtersimultaneous localization and mapping (SLAM)Gmapping algorithmadaptive resampling methods
分类号:
TP242.6
DOI:
10.3969/j.issn.1673-4785.201404024
文献标志码:
A
摘要:
传统Rao-Blackwellized粒子滤波器(RBPF)在移动机器人同步定位与地图构建(SLAM)研究中,存在算法复杂度过高、占用内存空间过多导致实时性不理想的问题,因此提出一种改进算法.在某一特定状态的一组粒子集中,粒子的统计特性是一致的,改进算法从中选取一个代表粒子,进行卡尔曼更新步骤,并在同一粒子集中重复使用.同时结合Gmapping算法的建议分布和自适应重采样技术.实际Pioneer III移动机器人在机器人操作系统(ROS)平台上进行的实验表明,该方法在保证栅格地图精度的同时能提高系统的实时性,降低复杂度,提高运算速度.
Abstract:
As in the research of simultaneous localization and mapping (SLAM) of mobile robot applying traditional Rao-Blackwellized particle filter, the computational complexity is too high and memory space usage is too large, which causes poor real-time performance, an improved approach is proposed. Among a group of particles gathering in a particular state, the statistical properties of particles are identical. By applying the Kalman updating step to one representative particle in the group of particles, and using it repeatedly in the same group, the complexity is reduced and arithmetic speed is improved. Combining the proposed distribution and adaptive resampling methods from the Gmapping algorithm, the results of actual experiment carried out with Pioneer III robot and ROS platform illustrate that the real-time performance of the proposal could be enhanced while ensuring the quality of grid map.

参考文献/References:

[1] SMITH R, SELF M, CHEESEMAN P. Estimating uncertain spatial relationships in robotics[M]//COX I J, WILFONG G T. Autonomous Robot Vehicles. New York: Springer, 1990: 167-193.
[2] DISSANAYAKE G, HUANG S, WANG Z, et al. A review of recent developments in simultaneous localization and mapping[C]//6th International Conference on Industrial and Information Systems (ICIIS). Kandy, Sri Lanka, 2011: 477-482.
[3] SMITH R C, CHEESEMAN P. On the representation and estimation of spatial uncertainty[J]. The International Journal of Robotics Research, 1986, 5(4): 56-68.
[4] 张文玲, 朱明清, 陈宗海. 基于强跟踪UKF的自适应 SLAM 算法[J]. 机器人, 2010, 32(2): 190-195. ZHANG Wenling, ZHU Mingqing, CHEN Zonghai. An adaptive SLAM algorithm based on strong tracking UKF[J]. Robot, 2010, 32(2): 190-195.
[5] DOUCET A, De FREITAS N, MURPHY K, et al. Rao-Blackwellised particle filtering for dynamic Bayesian networks[C]//Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence. San Francisco, USA, 2000: 176-183.
[6] QU Liping, WANG Hongjian. An overview of robot SLAM problem[C]//International Conference on Consumer Electronics, Communications and Networks (CECNet). Xianning, China, 2011: 1953-1956.
[7] GRISETTI G, STACHNISS C, BURGARD W. Improved techniques for grid mapping with Rao-Blackwellized particle filters[J]. Robotics, 2007, 23(1): 34-46.
[8] 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: 1-6.
[9] GERKEY B. Gmapping.[EB/OL]. [2010-08-05]. http://wiki.ros.org/slam_gmapping.
[10] GRISETTI G, STACHNISS C, BURGARD W. Improving grid-based slam with Rao-Blackwellized particle filters by adaptive proposals and selective resampling[C]//Proceedings of the 2005 IEEE International Conference on Robotics and Automation. Barcelona, Spain, 2005: 2432-2437.
[11] DOUCET A, De FREITAS N, GORDON N. An introduction to sequential Monte Carlo methods[M]//DOUCET A, DE FREITAS N, GORDON N. Sequential Monte Carlo Methods in Practice. New York: Springer, 2001: 3-14.
[12] De FREITAS N. Rao-Blackwellised particle filtering for fault diagnosis[C]//2002 IEEE Aerospace Conference Proceedings. Big Sky, USA, 2002, 4: 1767-1772.

相似文献/References:

[1]蔡自兴,王 勇,王 璐.基于角点聚类的移动机器人自然路标检测与识别[J].智能系统学报,2006,1(01):52.
 CAI Zi-xing,WANG Yong,WANG Lu.Corner clustering based detection and recognition of natural landmark for mobile robot[J].CAAI Transactions on Intelligent Systems,2006,1(03):52.
[2]杨甜甜,刘志远,陈 虹,等.移动机器人编队控制的现状与问题[J].智能系统学报,2007,2(04):21.
 YANG Tian-tian,LIU Zhi-yuan,CHEN Hong,et al.Formation control of mobile robots: state and open prob lems[J].CAAI Transactions on Intelligent Systems,2007,2(03):21.
[3]李润伟,蔡自兴,童宇,等.基于ATM的提高狭窄环境探测精度的改进方法[J].智能系统学报,2008,3(04):283.
 LI Run-wei,CAI Zi-xing,TONG Yu.Improving the accuracy of exploring the narrow environment by using ATM[J].CAAI Transactions on Intelligent Systems,2008,3(03):283.
[4]霍成立,谢 凡,秦世引.面向室内移动机器人的无迹滤波实时导航方法[J].智能系统学报,2009,4(04):295.
 HUO Cheng-li,XIE Fan,QIN Shi-yin.A case study in realtime UKFbased navigation for indoor autonomous travel of mobile robots[J].CAAI Transactions on Intelligent Systems,2009,4(03):295.
[5]海 丹,李 勇,张 辉,等.无线传感器网络环境下基于粒子滤波的移动机器人SLAM算法[J].智能系统学报,2010,5(05):425.[doi:10.3969/j.issn.1673-4785.2010.05.008]
 HAI Dan,LI Yong,ZHANG Hui,et al.Simultaneous localization and mapping of a mobile robot in wireless sensor networks based on particle filtering[J].CAAI Transactions on Intelligent Systems,2010,5(03):425.[doi:10.3969/j.issn.1673-4785.2010.05.008]
[6]房立金,王洪光.架空线移动机器人行走越障特点[J].智能系统学报,2010,5(06):492.
 FANG Li-jin,WANG Hong-guang.Research on the characteristics of the movement and obstacleclearing processes of a wiresuspended mobile robot[J].CAAI Transactions on Intelligent Systems,2010,5(03):492.
[7]任立敏,王伟东,杜志江.移动机器人队形控制关键技术及其进展[J].智能系统学报,2013,8(05):381.[doi:10.3969/j.issn.1673-4785.201302011]
 REN Limin,WANG Weidong,DU Zhijiang.Key technologies and development of formation control of mobile robots[J].CAAI Transactions on Intelligent Systems,2013,8(03):381.[doi:10.3969/j.issn.1673-4785.201302011]
[8]贺超,刘华平,孙富春,等.采用Kinect的移动机器人目标跟踪与避障[J].智能系统学报,2013,8(05):426.[doi:10.3969/j.issn.1673-4785.201301028]
 HE Chao,LIU Huaping,SUN Fuchun,et al.Target tracking and obstacle avoidance of mobile robot using Kinect[J].CAAI Transactions on Intelligent Systems,2013,8(03):426.[doi:10.3969/j.issn.1673-4785.201301028]
[9]阮晓钢,庞涛,张晓平,等.一种基于情感智能的机器人自主趋光行为研究[J].智能系统学报,2015,10(01):97.[doi:10.3969/j.issn.1673-4785.201312035]
 RUAN Xiaogang,PANG Tao,ZHANG Xiaoping,et al.Research on the autonomous phototaxis behavior of a robot based on emotion intelligence[J].CAAI Transactions on Intelligent Systems,2015,10(03):97.[doi:10.3969/j.issn.1673-4785.201312035]
[10]沈博闻,于宁波,刘景泰.仓储物流机器人集群的智能调度和路径规划[J].智能系统学报,2014,9(06):659.[doi:10.3969/j.issn.1673-4785.201312048]
 SHEN Bowen,YU Ningbo,LIU Jingtai.Intelligent scheduling and path planning of warehouse mobile robots[J].CAAI Transactions on Intelligent Systems,2014,9(03):659.[doi:10.3969/j.issn.1673-4785.201312048]

备注/Memo

备注/Memo:
收稿日期:2014-4-15;改回日期:。
基金项目:国家自然科学基金资助项目(51075420);重庆市教委科学技术研究项目(KJ120519).
作者简介:罗元,女,1972年生,教授,博士,主要研究方向为机器视觉、数字图像处理.获得国家发明专利6项,重庆市科技进步奖1项.发表学术论文50余篇,被SCI、EI检索20余篇.余佳航,男,1990年生,硕士研究生,主要研究方向为移动机器人导航.汪龙峰,男,1989年生,硕士研究生,主要研究方向为移动机器人导航.
通讯作者:余佳航. E-mail: tracy_the_1@126.com.
更新日期/Last Update: 2015-07-15