Monte Carlo localization for mobile robots in dynamic environments

Mobile robot localization is the problem of determining a robot's pose from sensor data. This thesis presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples) which approximate the posterior under a common Bayesian formulation of the localization problem. The MCL algorithm does not work well in dynamic environments. Thus, building on the basic MCL algorithm, this thesis develops a dynamic version of the algorithm, which applies filtering techniques to filter out the unexpected data and work well in dynamic environments. Systematic empirical results illustrate the robustness and computational efficiency of the approach.