Paper Info
Title
Indoor SLAM application using geometric and ICP matching methods based on line features.
Abstract
This study presents an autonomous guided vehicle (AGV) with simultaneous localization and map building (SLAM) based on matching method, and extended Kalman filter SLAM. In general, the AGV is a large mobile robot that is used in transportation to carry cargoes, and it is guided using wired or wireless guidance systems. The guidance system based AGV accounts for a majority of robots in the mobile robot industry. However, in semiconductor factories, landmarks are unavailable; hence, the existing system has not been used in the mentioned environments. Therefore, the SLAM technology is applied in the environments, and can guide the AGV without landmarks. However, the accuracy of the SLAM can be low owing to measurement error of sensors and a cumulative calculation caused by localization sensors. Therefore, the accuracy is frequently assumed to be incorrect; moreover, the accuracy of the built map is low. In order to solve the problems, this study proposes the AGV with the SLAM based on matching methods; two matching method; geometric matching method and iterative closest point algorithm. The performance of the proposed method is compared with typical methods such as singular value decomposition / RIGID transformation based technologies using feature-point-based SLAM and is compared with the aforementioned two methods using the extended Kalman filter SLAM. The proposed method is more efficient than the typical methods used in the comparison.
Year
DOI
Venue
2018
10.1016/j.robot.2017.11.011
Robotics and Autonomous Systems
Keywords
Field
DocType
Indoor SLAM,Geometric matching method,ICP matching method,EKF SLAM
Computer vision,Singular value decomposition,Extended Kalman filter,Wireless,Computer science,Rigid transformation,Artificial intelligence,Guidance system,Robot,Mobile robot,Iterative closest point
Journal
Volume
ISSN
Citations 
100
0921-8890
4
PageRank 
References 
Authors
0.41
18
3
Name
Order
Citations
PageRank
Hyunhak Cho1184.29
Eun-Kyeong Kim262.59
Sungshin Kim321064.17