This paper addresses the problem of feature-based robot localization in large-size environments. With recent progress in SLAM techniques, it has become crucial for a robot to estimate the self-position in real-time with respect to a large-size map that can be incrementally build by other mapper robots. Self-localization using large-size maps have been studied in literature, but most of them assume that a complete map is given prior to the self-localization task. In this paper, we present a novel scheme for robot localization as well as map representation that can successfully work with large-size and incremental maps. This work combines our two previous works on incremental methods, iLSH and iRANSAC, for appearance-based and position-based localization.

This study provides an investigation of scalability of mobile robot localization. In recent years, inference algorithms based on map-matching have proved their superior performance in large-scale environments. In this paper, the scalability is augmented by an ANN retrieval of high-dimensional descriptive features. The proposed algorithm is then exhaustively evaluated using large-size real maps, including over 100K feature maps.