On the scalability of robot localization using high-dimensional features

Abstract

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.