Publication Type : Book Chapter
Thematic Areas : TIFAC-CORE in Cyber Security
Publisher : Intelligent Computing, Communication and Devices: Proceedings of ICCD 2014, Volume 1
Source : Intelligent Computing, Communication and Devices: Proceedings of ICCD 2014, Volume 1, Springer India, Number 308, New Delhi, p.285–291 (2015)
Url : http://dx.doi.org/10.1007/978-81-322-2012-1_30
ISBN : 9788132220121
Keywords : Gaussian Mixture Model, Pose graph data structure, Probabilistic robotics, Simultaneous localization and mapping (SLAM)
Campus : Coimbatore
School : Centre for Cybersecurity Systems and Networks, School of Engineering
Center : TIFAC CORE in Cyber Security
Department : Computer Science, cyber Security
Year : 2015
Abstract : Simultaneous localization and mapping (SLAM) problem helps a mobile robot in identifying its own position by providing an autonomously built map. This work proposes a software and hardware approach for online mobile robotic systems, which is capable of performing SLAM. The mapping of unknown environment with low-cost sensors, incorporating probabilistic method, is the highlight of this work. The hardware system comprises of a multisensor mobile robot developed on the ARM Cortex platform. The software part mainly incorporates pose graph data structure blended with mixture model, which is further optimized by stochastic gradient descent method.
Cite this Research Publication : R. S. Anoop, Dr. Gireesh K. T., and Saisuriyaa, G., “A Probabilistic Method Toward SLAM for Mobile Robotic Systems”, in Intelligent Computing, Communication and Devices: Proceedings of ICCD 2014, Volume 1, L. C. Jain, Patnaik, S., and Ichalkaranje, N., Eds. New Delhi: Springer India, 2015, pp. 285–291.