Before revision:  The popular method, Monte Carlo Localization (MCL) addresses the problem by sampling uniformly ] over the state space , which is unfortunately inefficient when the environment is large .
After revision:  The popular method, Monte Carlo Localization (MCL) addresses the problem by iteratively updating a set of particles with a "sampling-weighting" loop. Sampling is decisive to the performance of MCL ] over the state space , which is unfortunately inefficient when the environment is large .