Particle filter based robust simultaneous localization and map building for mobile robots

Robust simultaneous localization and map building (SLAM) is a key issue for mobile robot in presence of faults. In the paper, an adaptive particle filter is designed to achieve robust SLAM for wheeled mobile robot when the robot is subjected to faults such as sensor faults and wheel slippage. Firstly, the kinematics models of wheeled mobile robots and the measurement models of laser range finder are derived, five kinds of residual features are extracted and faults are detected according residual features, and the proposal distribution is adaptively constructed according to residual features. Secondly, an adaptive mutation scheme is designed to recover the diversity of the particles after resampling stage. Lastly, the presented method is testified in a real mobile robot.