Concurrent matching, localization and map building using invariant features

A common way of localization in robotics is using triangulation on a system composed of a sensor and some landmarks (which can be artificial or natural). First, when no identifying marks are set on the landmarks, their identification by a robust algorithm is a complex problem which may be solved thanks to correspondence graphs. Second, when the localization system has no a priori information about its environment, it has to build its own map in parallel with estimating its position, a problem known as the simultaneous localization and mapping (SLAM). Recent works have proposed to solve this problem based on building a map made of invariant features. This paper describes the algorithms and data structure needed to deal with landmark matching, robot localization and map building in a single efficient process, unifying the previous approaches. Experimental results are presented using an outdoor robot car equipped with a 2D scanning laser sensor.