Publication Type:

Journal Article


Communications in Computer and Information Science, Volume 101, Kochi, Kerala, p.602-605 (2010)





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


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.


cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@7cf26227 ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@d0a2e87 Through org.apache.xalan.xsltc.dom.DOMAdapter@1774a7c1; Conference Code:98068

Cite this Research Publication

JaDivya Udayan, Kumar, TbGireesh, 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.