基于小生境遗传优化的Rao-Blackwellised SLAM算法
同步定位与地图构建（SLAM）是实现机器人自主定位的核心问题之一,Rao-Blackwellised粒子滤波器（RBPF）作为一种SLAM定位的有效方法,被广泛应用在实时定位领域中,但由于其随着粒子数目的增加会频繁重采样从而导致粒子退化问题。为了解决该问题,改善SLAM性能,提出了一种基于改进小生境遗传优化的RBPF SLAM算法INGO-RBPF,采用改进的Rao-Blackwellised粒子滤波器解决SLAM路径估计问题,采用扩展卡尔曼滤波器解决SLAM地图估计问题。最后通过MATLAB仿真表明INGO-RBPF算法具有较高的估计精度和稳定性,抗干扰能力较强,定位较准确,比较适合应用在S...
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| Published in | 计算机应用研究 Vol. 34; no. 8; pp. 2368 - 2371 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
东华大学 数字化纺织服装技术教育部工程研究中心,上海 201620
2017
东华大学 信息科学与技术学院,上海,201620%东华大学 信息科学与技术学院,上海 201620 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-3695 |
| DOI | 10.3969/j.issn.1001-3695.2017.08.030 |
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| Abstract | 同步定位与地图构建（SLAM）是实现机器人自主定位的核心问题之一,Rao-Blackwellised粒子滤波器（RBPF）作为一种SLAM定位的有效方法,被广泛应用在实时定位领域中,但由于其随着粒子数目的增加会频繁重采样从而导致粒子退化问题。为了解决该问题,改善SLAM性能,提出了一种基于改进小生境遗传优化的RBPF SLAM算法INGO-RBPF,采用改进的Rao-Blackwellised粒子滤波器解决SLAM路径估计问题,采用扩展卡尔曼滤波器解决SLAM地图估计问题。最后通过MATLAB仿真表明INGO-RBPF算法具有较高的估计精度和稳定性,抗干扰能力较强,定位较准确,比较适合应用在SLAM实时定位中。 |
|---|---|
| AbstractList | TP301.6; 同步定位与地图构建(SLAM)是实现机器人自主定位的核心问题之一,Rao-Blackwellised粒子滤波器(RBPF)作为一种SLAM定位的有效方法,被广泛应用在实时定位领域中,但由于其随着粒子数目的增加会频繁重采样从而导致粒子退化问题.为了解决该问题,改善SLAM性能,提出了一种基于改进小生境遗传优化的RBPF SLAM算法INGO-RBPF,采用改进的Rao-Blackwellised粒子滤波器解决SLAM路径估计问题,采用扩展卡尔曼滤波器解决SLAM地图估计问题.最后通过MATLAB仿真表明INGO-RBPF算法具有较高的估计精度和稳定性,抗干扰能力较强,定位较准确,比较适合应用在SLAM实时定位中. 同步定位与地图构建（SLAM）是实现机器人自主定位的核心问题之一,Rao-Blackwellised粒子滤波器（RBPF）作为一种SLAM定位的有效方法,被广泛应用在实时定位领域中,但由于其随着粒子数目的增加会频繁重采样从而导致粒子退化问题。为了解决该问题,改善SLAM性能,提出了一种基于改进小生境遗传优化的RBPF SLAM算法INGO-RBPF,采用改进的Rao-Blackwellised粒子滤波器解决SLAM路径估计问题,采用扩展卡尔曼滤波器解决SLAM地图估计问题。最后通过MATLAB仿真表明INGO-RBPF算法具有较高的估计精度和稳定性,抗干扰能力较强,定位较准确,比较适合应用在SLAM实时定位中。 |
| Abstract_FL | Simulation localization and mapping (SLAM) is one of the key problems in realizing robot self-navigation.As an effective method for SLAM location, it widely used Rao-Blackwellised particle filter(RBPF) in the field of real time location.However, the RBPF behavior of frequent resampling results in particle impoverishment problem along with particles increased.In order to solve the problem and improve the algorithm performance, this paper proposed a RBPF SLAM algorithm based on improved niched genetic optimization (INGO-RBPF).The INGO-RBPF algorithm solves the robot path estimation using improved Rao-Blackwellised particle filter(PF), and solves the map estimation using extended Kalman filter (EKF).Finally the MATLAB simulations prove that INGO-RBPF performs well on estimated accuracy, stability, disturbance and location accuracy, and therefore it is suitable to apply in SLAM real-time location. |
| Author | 陈建军 廖小飞 吴赟 陈光 庄新闯 |
| AuthorAffiliation | 东华大学信息科学与技术学院,上海201620 东华大学数字化纺织服装技术教育部工程研究中心,上海201620 |
| AuthorAffiliation_xml | – name: 东华大学 信息科学与技术学院,上海,201620%东华大学 信息科学与技术学院,上海 201620;东华大学 数字化纺织服装技术教育部工程研究中心,上海 201620 |
| Author_FL | Zhuang Xinchuang Liao Xiaofei Wu Yun Chen Guang Chen Jianjun |
| Author_FL_xml | – sequence: 1 fullname: Chen Jianjun – sequence: 2 fullname: Liao Xiaofei – sequence: 3 fullname: Wu Yun – sequence: 4 fullname: Chen Guang – sequence: 5 fullname: Zhuang Xinchuang |
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| ClassificationCodes | TP301.6 |
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| DocumentTitleAlternate | Rao-Blackwellised SLAM based on niched genetic optimized method |
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| Keywords | Rao-Blackwellised粒子滤波器 小生境遗传算法 Rao-Blackwellised particle filter 同步定位与地图创建(SLAM) simulation location and mapping (SLAM) niched genetic algorithm INGO-RBPF |
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| Notes | 51-1196/TP simulation location and mapping （SLAM） ; Rao-Blackwellised particle filter; niched genetic algorithm; INGO- RBPF Simulation localization and mapping （SLAM） is one of the key problems in realizing robot self-navigation. As an effective method for SLAM location, it widely used Rao-Blackwellised particle filter（RBPF） in the field of real time location. However, the RBPF behavior of frequent resampling results in particle impoverishment problem along with particles increased. In order to solve the problem and improve the algorithm performance, this paper proposed a RBPF SLAM algorithm based on improved niched genetic optimization （INGO-RBPF）. The INGO-RBPF algorithm solves the robot path estimation using im- proved Rao-Blackwellised particle filter（ PF）, and solves the map estimation using extended Kalman filter （EKF）. Finally the MATLAB simulations prove that INGO-RBPF performs well on estimated accuracy, stability, disturbance and location accura- cy, and therefore it is suitable to apply in SLAM real- |
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| PublicationTitle | 计算机应用研究 |
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| PublicationYear | 2017 |
| Publisher | 东华大学 数字化纺织服装技术教育部工程研究中心,上海 201620 东华大学 信息科学与技术学院,上海,201620%东华大学 信息科学与技术学院,上海 201620 |
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| Snippet | 同步定位与地图构建（SLAM）是实现机器人自主定位的核心问题之一,Rao-Blackwellised粒子滤波器（RBPF）作为一种SLAM定位的有效方法,被广泛应用在实时定位领域中,但由于其... TP301.6; 同步定位与地图构建(SLAM)是实现机器人自主定位的核心问题之一,Rao-Blackwellised粒子滤波器(RBPF)作为一种SLAM定位的有效方法,被广泛应用在实时定位领域中,但由... |
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| SubjectTerms | INGO-RBPF Rao-Blackwellised粒子滤波器 同步定位与地图创建（SLAM） 小生境遗传算法 |
| Title | 基于小生境遗传优化的Rao-Blackwellised SLAM算法 |
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