Localization with non-unique landmark observations

Abstract

In the probabilistic robot localization problem, when the associations of observations with the landmarks in the map are given, the solution is straightforward. However, when the observations are non-unique (e.g. the association with the map is not given) the problem becomes more challenging. In the Standard Platform League (SPL) and other similar categories of RoboCup, as the field setups evolve over years, the observations become less informative. In the localization level, we have to seek solutions with non-unique landmark observations. In this paper, we established the probabilistic model of the problem and showed the difficulty of optimal solution. After that, we introduce our importance sampling based approximate solution and implicit hypothesis pruning. We give results from simulation tests in the SPL setup using corners and goal bar observations and discuss characteristics of our approach. © 2011 Springer-Verlag.