全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
-  2017 

Simultaneous Localization and Mapping System Based on Labels
Simultaneous Localization and Mapping System Based on Labels

DOI: 10.15918/j.jbit1004-0579.201726.0413

Keywords: simultaneous localization and mapping (SLAM) extended Kalman filter (EKF) quick response(QR) codes artificial landmarks
simultaneous localization and mapping (SLAM) extended Kalman filter (EKF) quick response(QR) codes artificial landmarks

Full-Text   Cite this paper   Add to My Lib

Abstract:

In this paper a label-based simultaneous localization and mapping (SLAM) system is proposed to provide localization to indoor autonomous robots. In the system quick response(QR) codes encoded with serial numbers are utilized as labels. These labels are captured by two webcams, then the distances and angles between the labels and webcams are computed. Motion estimated from the two rear wheel encoders is adjusted by observing QR codes. Our system uses the extended Kalman filter (EKF) for the back-end state estimation. The number of deployed labels controls the state estimation dimension. The label-based EKF-SLAM system eliminates complicated processes, such as data association and loop closure detection in traditional feature-based visual SLAM systems. Our experiments include software-simulation and robot-platform test in a real environment. Results demonstrate that the system has the capability of correcting accumulated errors of dead reckoning and therefore has the advantage of superior precision.
In this paper a label-based simultaneous localization and mapping (SLAM) system is proposed to provide localization to indoor autonomous robots. In the system quick response(QR) codes encoded with serial numbers are utilized as labels. These labels are captured by two webcams, then the distances and angles between the labels and webcams are computed. Motion estimated from the two rear wheel encoders is adjusted by observing QR codes. Our system uses the extended Kalman filter (EKF) for the back-end state estimation. The number of deployed labels controls the state estimation dimension. The label-based EKF-SLAM system eliminates complicated processes, such as data association and loop closure detection in traditional feature-based visual SLAM systems. Our experiments include software-simulation and robot-platform test in a real environment. Results demonstrate that the system has the capability of correcting accumulated errors of dead reckoning and therefore has the advantage of superior precision.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133