Adapting proposal distributions for accurate, efficient mobile robot localization

When performing probabilistic localization using a particle filter, a robot must have a good proposal distribution in which to distribute its particles. Once weighted by their normalized likelihood scores, these particles estimate a posterior distribution over the possible poses of the robot. This paper 1) introduces a new action model (the system of equations used to determine the proposal distribution at each time step) that can run on any differential drive robot, even from log file data, 2) investigates the results of different algorithms that modify the proposal distribution at each time step in order to obtain more accurate localization, 3) investigates the results of incrementally adapting the action model parameters based on recent localization results in order to obtain proposal distributions that better approximate the true posteriors. The results show that by adapting the action model over time and, when necessary, modifying the resulting proposal distributions at each time step, localization improves-the maximum likelihood score increases and, when possible, the percentage of wasted particles decreases