Laser-Based Localization with Sparse Landmarks

Self-localization in dynamic environments is a central problem in mobile robotics and is well studied in the literature. One of the most popular methods is the Monte Carlo Localization algorithm (MCL). Many deployed systems use MCL together with a laser range finder in well structured indoor environments like office buildings with a rather rich collection of landmarks. In symmetric environments like robotic soccer with sparse landmarks which are occluded by other robots, most of the time the standard method does not yield satisfying results. In this paper we propose a new heuristic weight function to integrate a full 360° sweep from a laser range finder and introduce so-called don't-care regions which allow to ignore some parts of the environment. The proposed method is not specific to robotic soccer and scales very well outside the soccer domain.