In this paper we propose an algorithm to deter- mine the location of a vehicle in an environment represented by a stochastic map, given a set of environment measure- ments 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 lin- ear with both the size of the stochastic map and the number of measurements. We demonstrate the practicality and ro- bustness of our approach with experiments in an outdoor environment.
['José L. Neira', 'Juan D. Tardós', 'José A. Castellanos']
Linear time vehicle relocation in SLAM