Monte Carlo localization for mobile robot with the improvement of particle filter

It is a key issue for Monte Carlo localization of mobile robot based on particle filter in mobile robot research. However, the existed problem with the SIS (sequential importance sampling) particle filter is the degeneracy phenomenon. Hence, two parts in particle filter are improved. One is to combine the resampling of particle filter with fuzzy map matching presented in advance to reduce the uncertainty influence. The other is to adopt the resampling self adaptation based on ESS (efficient sample size) and to introduce mixture Gaussian distribution to approximate proposal distribution so as to improve the sample weight computation in obtaining ESS to ensue the appropriate resampling times. The experiment is implemented with mobile robot MORCS-1 as experimental platform and its results prove the validity of the improvement of particle filter in the paper.