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Robust Global Localization Using Laser Reflectivity

Global localization, which determines an accurate global position without prior knowledge, is a fundamental requirement for a mobile robot. Map-based global localization gives a precise position by comparing a provided geometric map and current sensory data. Although 3D range data is preferable for 6D global localization in terms of accuracy and reliability, comparison with large 3D data is quite timeconsuming. On the other hand, appearance-based global localization, which determines the global position by comparing a captured image with recorded ones, is simple and suitable for real-time processing. However, this technique does not work in the dark or in an environment in which the lighting conditions change remarkably. We herein propose a two-step strategy, which combines map-based global localization and appearance-based global localization. Instead of camera images, which are used for appearance-based global localization, we use reflectance images, which are captured by a laser range finder as a byproduct of range sensing. The proposed method consists of two steps: i) reflectance images acquired by a laser range finder are used for rough estimation of global position based on the bagof-features (BoF) technique, and ii) precise global positions are determined automatically by the iterative closest points (ICP) algorithm for 3D range data. The effectiveness of the proposed technique is demonstrated through experiments in real environments.