LSH-RANSAC: An incremental scheme for scalable localization

Abstract— This paper addresses the problem of featurebased 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 largesize map that can be incrementally build by other mapper robots. Self-localization using large-size maps have been studied in litelature, 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 appearancebased and position-based localization. I. MOTIVATION Self-localization is an important task for mobile robot environments [1]–[3]. With recent progress in SLAM techniques, it becomes possible for a robot to obtain and use large-size environment maps that are incre...

• Type: Conference
• Year: 2009
• Where: ICRA
• Authors: Kenichi Saeki, Kanji Tanaka, Takeshi Ueda
• Keywords: ICRA 2009, Incremental Maps, Large-size Maps, Robot, Robotics