An Adaptive UKF-Based  Particle Filter for Mobile Robot SLAM

摘要

The mobile robot simultaneous localization and mapping (SLAM) in unknown environments has been considered to be an important and fundamental problem in the mobile robotics research domain. Nowadays most methods for SLAM are focused on probabilistic Bayesian estimation, this paper propose an unscented Kalman filter (UKF) assistant-proposal distribution (UKF-APD) particle algorithm,compute the Euclidean distance of particle approximate distribution to the UKF-APD, and take it as an adaptive particle-resampling criterion, the proposed algorithm can avoid particlespsila impoverishment and deviation to the real robot posterior distribution. Experimental results demonstrate the effectiveness of the proposed algorithm.