Mobile robot self-localization system based on multi-sensor information fusion in indoor environment

Abstract

Robot self-localization is a fundamental problem for mobile robots and various kinds of algorithms and sensors are used for indoor robot self-localization. However, a single kind of sensor or algorithm usually cannot reach the ideal performance in different environments. Thus this paper proposes a robot selflocalization algorithm based on multi-sensor information fusion (MSIF) and realizes the corresponding indoor robot self-localization system. To realize the whole system, two subsystems are constructed which are the gyro and encoderbased subsystem and the laser rangefinder and gyro-based subsystem. In these two subsystems, dead reckoning algorithm is used in the former one, and ranging selflocalization, together with map matching algorithm, is used in the latter one. With the information acquired by the two subsystems, Kalman filter algorithm is used to obtain the optimal estimation of the robot localization. The experimental results show that the system and the algorithms work well in indoor environments.