提出了一种移动机器人室、内外无缝定位方法，利用全球定位系统(GPS)、惯性导航单元(IMU)和蓝牙信标(iBeacon)作为传感器，通过智能终端的惯性传感器对准建立移动机器人运动模型，推导出系统状态方程和观测方程；利用iBeacon/IMU和GPS/IMU组合导航系统的观测噪声建立系统的非线性模型，采用粒子滤波作为交互式多模型匹配模滤波器，针对模型切换频繁引入的不匹配模型竞争，采用误差压缩率的加权幂在线调整Markov参数.结果表明，与传统的交互式多模型粒子滤波(IMM-PF)算法、单模型算法和交互式多模型卡尔曼滤波(IMM-KF)算法相比，所提出的方法在观测噪声干扰的情况下可以自动调整系统模型，减小了不匹配模型的影响，提高了定位精度，实现了移动机器人的室内、外无缝定位.
A multiple model which is suitable for indoor and outdoor application is presented based on loca-lization algorithm. Due to big difference between indoor and outdoor environments, there hardly exists a general localization algorithm for the two environments. In this work, a switched scheme is proposed to deal with this problem. Because of nonlinearity in measurement, method based on particle filtering is adopted for the mode matching filter. In time update, movement between two consecutive frames is provided by an inertial measurement unit (IMU). In measurement update, global positioning system (GPS) and iBeacon are used as observers in outdoor and indoor, respectively. A weighted error compression ratio which can adjust Markovian parameters online is proposed to deal with mismatch of competition model resulted by model switching frequently. Thus the localization precision can be guaranteed in situation of frequent switch between indoor and outdoor. Experimental results demonstrate precision and robustness of the proposed method which outperforms some state-of-the-art methods (e.g., IMM-PF, single model algorithm, IMM-KF, etc), especially when observation noise exists.
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