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Publication Type : Journal Article
Publisher : Communications in Computer and Information Science
Source : Communications in Computer and Information Science, Volume 101, Kochi, Kerala, p.602-605 (2010)
ISBN : 9783642157653
Keywords : Autonomous Mobile Robot, Information technology, Localization, Mobile robots, Monte Carlo methods, Multi-dimensional state, Particle filter, Pose estimation, Robot applications, Robot localization, Robotics, Sensor fusion, Sensor measurements, Sensors
Campus : Coimbatore
School : Centre for Cybersecurity Systems and Networks, School of Engineering
Center : TIFAC CORE in Cyber Security
Department : Computer Science, cyber Security
Year : 2010
Abstract : Perception and pose estimation are still some of the key challenges in the area of robotics, and hence the basic requirement for an autonomous mobile robot is its capability to elaborate the sensor measurements to localize itself with respect to a global reference frame. For this purpose the odometric values or the sensor measurements have to be fused together by means of particle filters. Earlier particle filters were limited to low-dimensional estimation problems, such as robot localization in known environments. More recently, particle filters are used in spaces with as many as 100,000 dimensions. This paper presents some of the recent innovations on the use of particle filters in robotics. © Springer-Verlag Berlin Heidelberg 2010.
Cite this Research Publication : JaDivya Udayan, Dr. Gireesh K. T., John, R. Mc, Poornaselvan, K. Jd, and Lakshmanan, S. Aa, “Mobile Robot Pose Estimation Based on Particle Filters for Multi-dimensional State Spaces”, Communications in Computer and Information Science, vol. 101, pp. 602-605, 2010.