| 一种新的粒子滤波SLAM算法 |
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| 引用本文： | 郭剑辉,赵春霞.一种新的粒子滤波SLAM算法[J].计算机研究与发展,2008,45(5):853-860. |
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| 作者姓名： | 郭剑辉 赵春霞 |
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| 作者单位： | 南京理工大学计算机科学与技术学院,南京,210094 |
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| 基金项目： | 国家高技术研究发展计划(863计划) |
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| 摘 要： | 粒子滤波SLAM算法的复杂度与特征个数呈线性关系,对于大规模SLAM有明显的计算优势,但是这些算法不能长时间满足一致性要求.将边缘粒子滤波技术(marginal particle filtering,MPF)运用到SLAM技术中,并利用Unscented Kalman滤波(UKF)来计算提议分布,得到了一种新的粒子滤波SLAM算法.新算法避免了从不断增长的高维状态空间采样,非常有效地提高了算法中的有效粒子数,大大降低了粒子的权值方差,保证了粒子的多样性,同时也满足一致性要求.该算法克服了一般粒子滤波SLAM算法的缺点,性能优势十分明显.
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| 关 键 词： | 同时定位与地图创建 边缘粒子滤波 unscented Kalman滤波 有效粒子数 权值方差 一致性 |
| 修稿时间： | 2007年7月3日 |
A Novel Algorithm of Simultaneous Localization and Map Building (SLAM) with Particle Filter |
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| GUO Jianhui,Zhao Chunxia.A Novel Algorithm of Simultaneous Localization and Map Building (SLAM) with Particle Filter[J].Journal of Computer Research and Development,2008,45(5):853-860. |
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| Authors: | GUO Jianhui Zhao Chunxia |
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| Affiliation: | Guo Jianhui , Zhao Chunxia(College of Computer Science , Technology,Nanjing University of Science , Technology,Nanjing 210094) |
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| Abstract: | The computational complexity of the most popular particle filtering SLAM algorithms are linear proportional to the number of landmarks,which have obvious computational superiority for dense map or large-scale SLAM.However,there is no guarantee that the computed covariance will match the actual estimation errors,which is the true SLAM consistency problem.The lack of consistency of these algorithms will lead to filter divergence.In order to ensure consistency,a new particle filtering SLAM algorithm is propose... |
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| Keywords: | simultaneous localization and map building(SLAM) marginal particle filtering(MPF) unscented Kalman filtering(UKF) number of effective particles variance of particle weight consistency |
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