Fast Artificial Landmark Detection for Indoor Mobile Robots

• Autorzy: Dmitriy Kartashov , Arthur Huletski , Kirill Krinkin
• Języki publikacji: EN

Abstrakty

EN

Nowadays the big challenge in simultaneous localization and mapping (SLAM) of mobile robots is the creation of efficient and robust algorithms. Significant Number of SLAM algorithms rely on unique features or or use artificial landmarks received from camera images. Feature points and landmarks extraction from images have two significant drawbacks: CPU consumption and weak robustness depending on environment conditions. In this paper we consider performance issues for landmark detection, introduce a new artificial landmark design and fast algorithm for detecting and tracking them in arbitrary images. Also we provide results of performance optimization for different hardware platforms.(original abstract)

Słowa kluczowe

EN

Algorithms; Robotization

PL

Algorytmy; Robotyzacja

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2015
• Tom: 5
• Strony: 209--214

Twórcy

autor

Dmitriy Kartashov

• The Academic University Saint-Petersburg, Russia

autor

Arthur Huletski

• The Academic University Saint-Petersburg, Russia

autor

Kirill Krinkin

• St.Petersburg State Electrotechnical University "LETI"

Bibliografia

• R. Siegwart, I. R. Nourbakhsh, D. Scaramuzza, Introduction to Autonomous Mobile Robots. MIT Press, 2011, p. 453.
• International Standard ISO/IEC 18004:2000 Information technology - Automatic identification and data capture techniques - Bar code symbology - QR Code, 2000.
• Luiz F. F. Belussi, Nina S. T. Hirata, "Fast Component-Based QR Code Detection in Arbitrarily Acquired Images", Journal of Mathematical Imaging and Vision, vol.45, no.3, Mar. 2013, pp. 277-292.
• Gabriel Klimek, Zoltan Vamossy, "QR Code Detection Using Parallel Lines", Computational Intelligence and Informatics (CINTI), 2013 IEEE 14th International Symposium, Nov. 2013, pp. 477-481.
• Yunhua Gu, Weixiang Zhang, "QR Code Recognition Based On Image Processing", International Conference on Information Science and Technology, Mar. 2011, pp. 734-736.
• Yue Liu, Mingjun Liu, "Automatic Recognition Algorithm of Quick Response Code Based on Embedded System", In. proc. of the Sixth International Conference on Intelligent Systems Design and Applications, Oct. 2006, pp. 783-788.
• Hao Wu, Guohui Tian, Peng Duan, Sen Sang, "The Design of a Novel Artificial Label for Robot Navigation", Proceedings of 2013 Chineseintelligent Automation Conference, pp. 479-486.
• Kuk-Jin Yoon, Gi-Jeong Jang, Sung-Ho Kim, In-So Kweon, "Fast Landmark Tracking and Localization Algorithm for the Mobile Robot Self-Localization", IFAC Workshop on Mobile Robot Technology, 2001, pp. 190-195.
• Antoni Buades, Bartomeu Coll, Jean-Michel Morel, "A non-local algorithm for image denoising", In proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Jun. 2005, pp. 60-65.
• OpenCV website, http://opencv.org
• ROS.org - Powering the world's robots, http://www.ros.org
• Raspberry Pi, http://www.raspberrypi.org

Identyfikatory

DOI

10.15439/2015F232