Evaluation of Likelihood of Measurement Model Based on Hough Transform for Global Localization

Abstract

Probabilistic robotics uses Bayesian formulation to solve global localization. An example is Monte Carlo localization (MCL), which spreads particles uniformly over an environment. While KLD-sampling may adaptively decrease the number of particles as the localization proceeds, its first measurement update would take relatively long computation time. On the other hand, approaches that use Hough Transform may have computational advantage. But the way they vote and localize seem to be heuristically determined. In this paper, the method that combines both approaches is proposed. The proposed method computes likelihood of measurement model based on Hough Transform and uses MCL to localize robots. A simple simulated experiment suggests that the proposed method may compute the first measurement update faster than MCL with the usual way to compute likelihood, if particles are initially placed according to a grid on a pose space.