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

被引：94

作者：

Kim, Giseop ^{[1
]}

Park, Byungjae ^{[2
]}

Kim, Ayoung ^{[1
]}

机构：

[1] Korea Adv Inst Sci & Technol, Dept Civil & Environm Engn, Daejeon 34141, South Korea

[2] ETRI, Intelligent Robot Syst Res Grp, Daejeon 34129, South Korea

来源：

IEEE ROBOTICS AND AUTOMATION LETTERS | 2019年 / 4卷 / 02期

关键词：

Localization; range sensing; SLAM; PLACE RECOGNITION;

D O I：

10.1109/LRA.2019.2897340

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

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.

引用

页码：1948 / 1955

页数：8