赵立军, 孙立宁, 李瑞峰, 葛连正. 室内环境下同步定位与地图创建改进算法[J]. 机器人, 2009, 31(5): 438-444.
引用本文: 赵立军, 孙立宁, 李瑞峰, 葛连正. 室内环境下同步定位与地图创建改进算法[J]. 机器人, 2009, 31(5): 438-444.
ZHAO Lijun, SUN Lining, LI Ruifeng, GE Lianzheng. On an Improved SLAM Algorithm in Indoor Environment[J]. ROBOT, 2009, 31(5): 438-444.
Citation: ZHAO Lijun, SUN Lining, LI Ruifeng, GE Lianzheng. On an Improved SLAM Algorithm in Indoor Environment[J]. ROBOT, 2009, 31(5): 438-444.

室内环境下同步定位与地图创建改进算法

On an Improved SLAM Algorithm in Indoor Environment

  • 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.

     

    Abstract: 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.

     

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