In this paper we propose an algorithm to determine the location of a vehicle in an environment represented by a stochastic map, given a set of environment measurements obtained by a sensor mounted on the vehicle. We show that the combined use of (1) geometric constraints considering feature correlation, (2) joint compatibility, (3) random sampling and (4) locality, make this algorithm linear with both the size of the stochastic map and the number of measurements. We demonstrate the practicality and robustness of our approach with experiments in an outdoor environment