1-Day Learning, 1-Year Localization: Long-Term LiDAR Localization Using Scan Context Image

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In this letter, we present a long-term localization method that effectively exploits the structural information of an environment via an image format. The proposed method presents a robust year-round localization performance even when learned in just a single day. The proposed localizer learns a point cloud descriptor, named Scan Context Image (SCI), and performs robot localization on a grid map by formulating the place recognition problem as place classification using a convolutional neural network. Our method is faster than existing methods proposed for place recognition because it avoids a pairwise comparison between a query and scans in a database. In addition, we provide thorough validations using publicly available long-term datasets, the NCLT dataset and the Oxford RobotCar dataset, and show that the Scan Context Image (SCI) localization attains consistent performance over a year and outperforms existing methods.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2019-04
Language
English
Article Type
Article
Citation

IEEE ROBOTICS AND AUTOMATION LETTERS, v.4, no.2, pp.1948 - 1955

ISSN
2377-3766
DOI
10.1109/LRA.2019.2897340
URI
http://hdl.handle.net/10203/253952
Appears in Collection
CE-Journal Papers(저널논문)
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