Robust Robot Monte Carlo Localization
鲁棒的机器人蒙特卡洛定位算法

A robot localization algorithm based on particle filter is presented.Firstly,in order to improve the filtering effect and decrease the number of particles needed,one parallel extended Kalman filter is used as the proposal density of particle filter,thus partial observation information can be infused into the filtering process.Secondly,in order to enhance the particles' refining capacity,one improved Markov chain Monte Carlo (MCMC) resampling method with variable boundary of proposal density is put forward.Finally,the robot localization algorithm with the improved MCMC resampling is established,thus the effect of particle impoverishment can be decreased and the localization accuracy can be improved.Experiment results show that this algorithm has the advantages in computational complexity,localization accuracy and robustness.