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基于压缩型EKF的SLAM改进算法 被引量：2

Improved SLAM Algorithm Based on Compressed-EKF
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摘要 针对基于压缩型扩展卡尔曼滤波(CEKF)的SLAM算法在状态增广和地图管理两方面的不足,提出了一种改进算法(ICEKF算法)。该算法通过增广辅助系数矩阵即可快速完成状态增广,计算复杂度由O(N2)降低为O(NA),其中N和NA分别为全局和局部地图中的路标数。在地图管理上,ICEKF算法采用一种基于欧氏距离的局部地图动态选择方法,避免了CEKF算法对全局地图进行预先划分带来的路标分配等问题。仿真表明ICEKF算法在估计结果上与EKF算法具有一致的最优性,与CEKF算法相比计算量大大降低。 The compressed extended Kalman filter（CEKF）-based algorithm for simultaneous localization and mapping（SLAM）has low efficiency on state augment and map management.An improved algorithm（ICEKF）was proposed.The ICEKF algorithm achieves the state augment by only augmenting one auxiliary coefficient matrix,and the computational complexity is reduced from O（N2）to O（NA）,where N and NA denote the number of the landmarks in the global and local maps respectively.A Euclidian distance-based method for map management wa...
作者 张海强 窦丽华 方浩 陈杰
机构地区 北京理工大学信息科学与技术学院自动控制系
出处 《系统仿真学报》 CAS CSCD 北大核心 2009年第18期5668-5671,5680,共5页 Journal of System Simulation
基金 北京市教育委员会共建基金(100070522)
关键词 同步定位和地图创建(SLAM) 压缩型扩展卡尔曼滤波 状态增广 计算复杂度 simultaneous localization and mapping（SLAM） compressed extended Kalman filter state augment computational complexity
分类号 TP242 [自动化与计算机技术—检测技术与自动化装置]
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参考文献9

  • 1M W M Dissanayake G, Newman P, Clark S, et al. A solution to the simultaneous localization and map building (SLAM) problem [J]. IEEE Transactions on Robotics and Automation (SO018-9286), 2001, 17(3): 229-241.
  • 2Durrant-Whyte H, Tim B. Simultaneous localization and mapping: part I [J]. IEEE Robotics and Automation Magazine (S1070-9932), 2006, 13(2): 99-110.
  • 3鞠纯纯,何波,刘保龙,王永清.基于粒子滤波器的SLAM的仿真研究[J].系统仿真学报,2007,19(16):3715-3718. 被引量：8
  • 4Randall S, Matthew S, Peter C. Estimating Uncertain Spatial Relationships in Robotics [J]. Uncertainty in Artificial Intelligence (S0218-4885), 1988, 2(8): 435-461.
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  • 6Shoudong H, Dissanayake G. Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM [J]. IEEE Transactions on Robotics (S0018-9286), 2007, 23(5): 1036-1049.
  • 7Guivant J, Nebot E. Optimization of the Simultaneous Localization and Map-building Algorithm for Real-time Implementation [J]. IEEE Trans. on Robotics and Automation (S0018-9286), 2001, 17(3): 242-257.
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  • 9Guivant J, Nebot E. Solving computational and memory requirements of feature-based simultaneous localization and mapping algorithms [J]. IEEE Transactions on Robotics and Automation (S0018-9286), 2003, 19(4): 749-755.

二级参考文献17

  • 1刘保龙,何波,王永清,鞠纯纯.移动机器人未知环境地图构建仿真平台[J].系统仿真学报,2007,19(13):2940-2943. 被引量：10
  • 2G Dissnaayake, P Newman, S Clark, H F Durrant-Whyte, et al. Solution to the Simultaneous Localization and Map Building (SLAM) Problem [J]. IEEE Transactions of Robotics and Automation (S0018-9286), 2001, 17(3): 229-241.
  • 3R Smith, P Cheeseman. On the Representation and Estimation of Spatial Uncertainty [J]. Robotics Research (S0278-3649), 1986, 5(4): 231-238.
  • 4R Smith, Mself, P Cheeseman. Estimating uncertain Spatial Relationships in Robotics [J]. Uncertainty in Artificial Intelligence (S0218-4885), 1988, 2(8): 435 -461.
  • 5F Dellaert, D Fox, W Burgard, S Thrum Monte Carlo Localization for Mobile Robots [J]. IEEE Transactions of Robotics and Automation (S0018-9286), 1999, 2(5): 10-15.
  • 6Kevin Murphy. Bayesian Map Learning in Dynamic Environments [I]. NIPS (S0944-8535), 1999, 5(7): 82-88
  • 7S Thrun, Y Liu, D Koller, et al. Simultaneous Localization andMapping with Sparse Extended Information Filters [J]. Intemational Journal of Robotics Research (S0278-3649), 2004, 7(11): 67-75.
  • 8R Eustice, M Walter, J Leonard. Sparse Extended Information Filters Intelligent Robots and Systems [C]//IROS, Edmonton, Canada, 2005: 641-648.
  • 9C Estrada, J Neira J Tard. Hierarchical slam: Real-time accurate mapping of large environments [J]. IEEE Transactions on Robotics (S0018-9286), 2005, 4(7): 588-596.
  • 10J Guivant, J Nieto, F Masson, E Nebot. Navigation and Mapping in Large Unstructured Environments [J]. International Journal of Robotics Research (S0278-3649), 2004, 23(10): 449-472.

共引文献7

同被引文献20

引证文献2

二级引证文献7

系统仿真学报
2009年 第18期

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