ISSN 1000-1239 CN 11-1777/TP

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一种新的粒子滤波SLAM算法

郭剑辉 赵春霞   

  1. (南京理工大学计算机科学与技术学院 南京 210094) (dafeel2002@yahoo.com.cn)
  • 出版日期: 2008-05-15

A Novel Algorithm of Simultaneous Localization and Map Building (SLAM) with Particle Filter

Guo Jianhui and Zhao Chunxia   

  1. (College of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094)
  • Online: 2008-05-15

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.

关键词: 同时定位与地图创建, 边缘粒子滤波, unscentedKalman滤波, 有效粒子数, 权值方差, 一致性

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 proposed, which is based on the marginal particle filtering and using unscented Kalman filtering (UKF) to generate proposal distributions. The underlying algorithm operates directly on the marginal distribution, hence avoiding having to perform importance sampling on a space of growing dimension. Additionally, UKF can reduce linearization error and gain accurate proposal distributions. Compared with the common particle filtering SLAM methods, the new algorithm increases the number of effective particles and reduces variance of particles weight effectively. Also, it is consistent owing to the better particle diversity. As a result, it does not suffer from some shortcomings of existing particle methods for SLAM and has distinct superiority. Finally, plentiful simulations are carried out to evaluate the algorithm’s performance and the results indicate that the algorithm is valid.

Key words: simultaneous localization and map building (SLAM), marginal particle filtering (MPF), unscented Kalman filtering (UKF), number of effective particles, variance of particle weight , consistency