Multi-sensor Fusion Based Indoor Mobile Robot Localization

Abstract

In this paper, a multi-sensor fusion framework is proposed to solve the localization problem of mobile robot in indoor environments. To improve the localization accuracy, two kinds of fusion algorithms, namely extended Kalman filter(EKF) and Monte Carlo localization(MCL), are used and the motion model as well as the measurement model are selected according to the complexity of the environment, which is quantified by the minimum distance between the robot and the obstacles. In EKF fusion, the motion model is obtained by wheel odometer, and the measurement model is the combination of inertial measurement unit(IMU) and ultra-wideband(UWB) sensor. While in MCL fusion, the motion model switches between odometry and the output of EKF, and the measurement model switches between lidar and the combination of IMU and UWB sensor. Experimental results show that the localization effect of multi-sensor fusion is better than that of a single sensor. The improved MCL algorithm proposed in this paper is superior to the traditional Monte Carlo localization algorithm in both localization accuracy and convergence speed.