An Improved Adaptive Monte Carlo Localization Algorithm Integrated with a Virtual Motion Model

Abstract

Regarding the issue of high dependency on odometry in the adaptive Monte Carlo localization (AMCL) algorithm, an improved AMCL algorithm based on the normal distributions transform (NDT) and extended Kalman filter (EKF) is proposed. A virtual motion model is introduced into the AMCL framework to enable pose updates even when the robot has not moved. NDT is used for point cloud matching to estimate virtual displacement and calculate virtual control quantities, which are then fed into the motion model to predict and update particle states when the robot has not moved. Additionally, to avoid the negative impacts of encoder errors and wheel slippage on motion state estimation, the EKF algorithm integrates information from the wheel odometer and inertial measurement unit to estimate the robot's displacement, thereby improving localization accuracy and stability. The performance of the proposed algorithm was experimentally validated in both simulated and real environments and compared with other localization algorithms. Experimental results show that the proposed algorithm can effectively improve localization speed during the cold start phase and enhances localization accuracy and stability throughout the localization process. The proposed method is a potential method for improving the performance of mobile robot localization.