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室内环境下同步定位与地图创建改进算法 被引量：4

On an Improved SLAM Algorithm in Indoor Environment
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摘要 提出了一种室内环境下基于平方根无迹卡尔曼滤波(SRUKF)的同步定位与地图创建(SLAM)算法.该方法在每步迭代中采用平方根无迹粒子滤波器进行机器人状态估计,并引入平方根无迹卡尔曼滤波器定位路标,进而完成机器人状态和相应路标信息更新.将本文算法与机器人运动模型和红外标签观测模型结合进行了仿真和实验,结果表明,本算法在同步定位和地图创建过程中提高了机器人状态和路标估计的精度及稳定性. A new simultaneous localization and mapping （SLAM） algorithm based on the square root unscented Kalman filter （SRUKF） is proposed for indoor environments. This algorithm uses square root unscented particle filter for estimating the robot states in every iteration, meanwhile, introduces SRUKF to localize the estimated landmarks, and then updates the robot states and landmark information. The proposed algorithm is combined with the robot motion model and observation model of infrared tag in simulation and experiment, and the results show that the algorithm improves the accuracy and stability of the estimated robot state and landmarks in SLAM.
作者 赵立军 孙立宁 李瑞峰 葛连正
机构地区 哈尔滨工业大学机器人研究所
出处 《机器人》 EI CSCD 北大核心 2009年第5期438-444,共7页 Robot
基金 国家863计划资助项目(2007AA04Z221) 长江学者与创新团队发展计划资助项目(IRT0423)
关键词 移动机器人室内定位 FASTSLAM 平方根无迹卡尔曼滤波器 indoor localization for mobile robot fast simultaneous localization and mapping （FastSLAM） square root unscented Kalman filter （SRUKF）
分类号 TP24 [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12

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同被引文献20

  • 1庄严,王伟,王珂,徐晓东.移动机器人基于激光测距和单目视觉的室内同时定位和地图构建[J].自动化学报,2005,31(6):925-933. 被引量：55
  • 2Eluca A, Orolo G,Samson C.Feedback control of a nonholonomic car-like robot [ A ].Laboratoire d'Analye et d' Architecture des Syst - emes Centre National de la Recherche Scientique LAAS Report 97438 [ C ], 1998.
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  • 5刘和平,邓力,等.DSP原理及电机控制应用[M].北京:北京航空航天大学出版社,2006.
  • 6Smith R, Self M, Cheeseman P. Estimating uncertain spatial relationships in robotics [ C ]//Proceedings of the 2nd Annual Conference on Uncertainty in Artificial Intelligence, UAI'86, 1986:435 - 461.
  • 7Murphy K P. Bayesian map learning in dynamic environments [ C ]//NIPS:Advances in Neural Information Processing System. Denver: Morgan-Kaufmann, 1999:1 015- 1021.
  • 8Wang C, Thorpe C, Thrun S,et al. FASTSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges [ C] // Proc. of the Int. Joint Conf. on Artificial Intelligence. Acapulco Mexico: Morgan Kaufmann, 2003:1 151 -1 156.
  • 9Abdallah F, Gning A, Bonnifait P. Box particle filtering for nonlinear state estimation using interval analysis [ J ]. Automatica, 2008,44( 3 ) :807 - 815.
  • 10Jaulin L, Kieffer M, Didrit O, et al. Applied interval analysis with examples in parameter and state estimation, robust control. and robotics [ M ]. London: Springer-Verlag, 2001.

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机器人
2009年 第5期

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