Coarse-to-Fine Localization for a Mobile Robot Based on Place Learning With a 2-D Range Scan

This paper presents a new coarse-to-fine global localization approach based on place learning with a 2-D range scan; we use a support vector machine (SVM) to train a set of classifiers for place recognition. Exploiting the graphical property of generalized Voronoi graph extracted from an occupancy grid map, we segment the map into local places with a spectral clustering algorithm and wavefront propagation algorithm. A bag-of-words approach is employed to build feature vectors for training the SVM classifiers, which utilizes the shape context descriptors computed from each scan point. The global localization process consists of two stages: coarse localization and fine localization. Coarse localization from the SVM-based place recognition selects candidate places where the robot may be located, and fine localization computes the relative poses to the candidate places with fast spectral scan matching and estimates the correct robot pose with a particle filter algorithm, in which the candidate poses are exploited as seeds for initial random sampling. The feasibility of the proposed methods is demonstrated by the results of extensive simulations and experiments.